Abstract
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Computer sciences and related disciplines evolve around developing, evaluating, and applying algorithms. Typically, an algorithm is not developed from scratch, but uses and builds upon existing ones, which often are proposed and published in scholarly articles. The ability to capture this evolution relationship among these algorithms in scientific literature would not only allow us to understand how a particular algorithm is composed, but also shed light on large-scale analysis of algorithmic evolution through different temporal spans and thematic scales. We propose to capture such evolution relationship between two algorithms by investigating the knowledge represented in citation contexts, where authors explain how cited algorithms are used in their works. A set of heterogeneous ensemble machine-learning methods is proposed, where the combination of two base classifiers trained with heterogeneous feature types is used to automatically identify the algorithm usage relationship. The proposed heterogeneous ensemble methods achieve the best average F1 of 0.749 and 0.905 for fine-grained and binary algorithm citation function classification, respectively. The success of this study will allow us to generate a large-scale algorithm citation network from a collection of scholarly documents representing multiple time spans, venues, and fields of study.

Abstract
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A 3D model of abdominal aortic aneurysm (AAA) can provide useful anatomical information for clinical management and simulation. Thin-slice contiguous computed tomographic (CT) angiography is the best source of medical images for construction of 3D models, which requires segmentation of AAA in the images. Existing methods for segmentation of AAA rely on either manual process or 2D segmentation in each 2D CT slide. However, a traditional manual segmentation is a time consuming process which is not practical for routine use. The construction of a 3D model from 2D segmentation of each CT slice is not a fully satisfactory solution due to rough contours that can occur because of lack of constraints among segmented slices, as well as missed segmentation slices. To overcome such challenges, this paper proposes the 3D segmentation of AAA using the concept of variable neighborhood search by iteratively alternating between two different segmentation techniques in the two different 3D search spaces of voxel intensity and voxel gradient. The segmentation output of each method is used as the initial contour to the other method in each iteration. By alternating between search spaces, the technique can escape local minima that naturally occur in each search space. Also, the 3D search spaces provide more constraints across CT slices, when compared with the 2D search spaces in individual CT slices. The proposed method is evaluated with 10 easy and 10 difficult cases of AAA. The results show that the proposed 3D segmentation technique achieves the outstanding segmentation accuracy with an average dice similarity value (DSC) of 91.88%, when compared to the other methods using the same dataset, which are the 2D proposed method, classical graph cut, distance regularized level set evolution, and registration based geometric active contour with the DSCs of 87.57±4.52%, 72.47± 8.11%, 58.50± 8.86% and 76.21 ± 10.49%, respectively.

Abstract
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Medical image segmentation is essential for severaltasks including pre-treatment planning and tumor monitoring. Computed tomography (CT) is the most useful imaging modalityfor abdominal organs and tumors, with benefits of high imagingresolution and few motion artifacts. Unfortunately, CT imagescontain only limited information of intensity and gradient, whichmakes accurate segmentation a challenge. In this paper, wepropose a 2D segmentation method that applies the concept ofvariable neighborhood search (VNS) by iteratively alternatingsearch through intensity and gradient spaces. By alternatingbetween the two search spaces, the technique can escape localminima that occur when segmenting in a single search space. Themain techniques used in the proposed framework are graph-cutwith probability density function (GCPDF) and graph-cut basedactive contour (GCBAC). The presented method is quantitativelyevaluated on a public clinical dataset, which includes various sizesof liver tumor, kidney and spleen. The segmentation performanceis evaluated using dice similarity coefficient (DSC), Jaccardsimilarity coefficient (JSC), and volume difference (VD). Thepresented method achieves the outstanding segmentation perfor-mance with a DSC of 84.48±5.84%, 76.93±8.24%, 91.70±2.68%and 89.27±5.21%, for large liver tumor, small liver tumor, kidneyand spleen, respectively.

Abstract
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Targeted intervention and resource allocation are essential in effective control of infectious diseases, particularly those like malaria that tend to occur in remote areas. Disease prediction models can help support targeted intervention, particularly if they have fine spatial resolution. But, choosing an appropriate resolution is a difficult problem since choice of spatial scale can have a significant impact on accuracy of predictive models. In this paper, we introduce a new approach to spatial clustering for disease prediction we call complexity-based spatial hierarchical clustering. The technique seeks to find spatially compact clusters that have time series that can be well characterized by models of low complexity. We evaluate our approach with 2 years of malaria case data from Tak Province in northern Thailand. We show that the technique’s use of reduction in Akaike information criterion (AIC) and Bayesian information criterion (BIC) as clustering criteria leads to rapid improvement in predictability and significantly better predictability than clustering based only on minimizing spatial intra-cluster distance for the entire range of cluster sizes over a variety of predictive models and prediction horizons.

Abstract
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Differentiating dengue patients from other acute febrile illness patients is a great challenge among physicians. Several dengue diagnosis methods are recommended by WHO. The application of specific laboratory tests is still limited due to high cost, lack of equipment, and uncertain validity. Therefore, clinical diagnosis remains a common practice especially in resource limited settings. Bayesian networks have been shown to be a useful tool for diagnostic decision support. This study aimed to construct Bayesian network models using basic demographic, clinical, and laboratory profiles of acute febrile illness patients to diagnose dengue. Data of 397 acute undifferentiated febrile illness patients who visited the fever clinic of the Bangkok Hospital for Tropical Diseases, Thailand, were used for model construction and validation. The two best final models were selected: one with and one without NS1 rapid test result. The diagnostic accuracy of the models was compared with that of physicians on the same set of patients. The Bayesian network models provided good diagnostic accuracy of dengue infection, with ROC AUC of 0.80 and 0.75 for models with and without NS1 rapid test result, respectively. The models had approximately 80% specificity and 70% sensitivity, similar to the diagnostic accuracy of the hospital’s fellows in infectious disease. Including information on NS1 rapid test improved the specificity, but reduced the sensitivity, both in model and physician diagnoses. The Bayesian network model developed in this study could be useful to assist physicians in diagnosing dengue, particularly in regions where experienced physicians and laboratory confirmation tests are limited.

Abstract
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Teaching surgical decision making aims at enabling students to choose the most appropriate action relative to the patient’s situation and surgical objectives. This requires a deep understanding of causes and effects related to the surgical domain as well as being aware of key properties of the current situation. To develop an intelligent tutoring system (ITS) for teaching situated decision making in the domain of dental surgery, in this paper, we present a planning-based representation framework. This framework is capable of representing surgical procedural knowledge with respect to situation awareness and algorithms that utilize the representation to generate rich tutorial dialog. The design of the tutorial dialogs is based on an observational study of surgeons teaching in the operating room. An initial evaluation shows that generated interventions are as good as and sometimes better than those of experienced human instructors.

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Location-based services and crowdsourced applications provide support for governments and other groups for plague and small disaster intervention. In this work in progress paper we report on an extension of the Mobile4D application to aid the government of Lao People’s Democratic Republic (Lao PDR) in dealing with the current yellow-spined bamboo locust plague. That is, we introduce the general project and approach, the Mobile4D application and specifically its locust module, report on intermediate results, and illustrate next steps to extend the support capabilities to other problems, e.g., vector-borne diseases

Abstract
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Aortic aneurysm segmentation remains a challenge. Manual segmentation is a time-consuming process which is not practical for routine use. To address this limitation, several automated segmentation techniques for aortic aneurysm have been developed, such as edge detection-based methods, partial differential equation methods, and graph partitioning methods. However, automatic segmentation of aortic aneurysm is difficult due to high pixel similarity to adjacent tissue and a lack of color information in the medical image, preventing previous work from being applicable to difficult cases. This paper uses uses a variable neighborhood search that alternates between intensity-based and gradient-based segmentation techniques. By alternating between intensity and gradient spaces, the search can escape from local optima of each space. The experimental results demonstrate that the proposed method outperforms the other existing segmentation methods in the literature, based on measurements of dice similarity coefficient and jaccard similarity coefficient at the pixel level. In addition, it is shown to perform well for cases that are difficult to segment.

Abstract
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Targeted intervention and resource allocation are essential for effective malaria control, particularly in remote areas, with predictive models providing important information for decision making. While a diversity of modeling technique have been used to create predictive models of malaria, no work has made use of Bayesian networks. Bayes nets are attractive due to their ability to represent uncertainty, model time lagged and nonlinear relations, and provide explanations. This paper explores the use of Bayesian networks to model malaria, demonstrating the approach by creating village level models with weekly temporal resolution for Tha Song Yang district in northern Thailand. The networks are learned using data on cases and environmental covariates. Three types of networks are explored: networks for numeric prediction, networks for outbreak prediction, and networks that incorporate spatial autocorrelation. Evaluation of the numeric prediction network shows that the Bayes net has prediction accuracy in terms of mean absolute error of about 1.4 cases for 1 week prediction and 1.7 cases for 6 week prediction. The network for outbreak prediction has an ROC AUC above 0.9 for all prediction horizons. Comparison of prediction accuracy of both Bayes nets against several traditional modeling approaches shows the Bayes nets to outperform the other models for longer time horizon prediction of high incidence transmission. To model spread of malaria over space, we elaborate the models with links between the village networks. This results in some very large models which would be far too laborious to build by hand. So we represent the models as collections of probability logic rules and automatically generate the networks. Evaluation of the models shows that the autocorrelation links significantly improve prediction accuracy for some villages in regions of high incidence. We conclude that spatiotemporal Bayesian networks are a highly promising modeling alternative for prediction of malaria and other vector-borne diseases.

Abstract
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BACKGROUND AND OBJECTIVE: We address the problem of automated outcome assessment in a virtual reality (VR) simulator for endodontic surgery. Outcome assessment is an essential component of any system that provides formative feedback, which requires assessing the outcome, relating it to the procedure, and communicating in a language natural to dental students. This study takes a first step toward automated generation of such comprehensive feedback.
METHODS: Virtual reference templates are computed based on tooth anatomy and the outcome is assessed with a 3D score cube volume which consists of voxel-level non-linear weighted scores based on the templates. The detailed scores are transformed into standard scoring language used by dental schools. The system was evaluated on fifteen outcome samples that contained optimal results and those with errors including perforation of the walls, floor, and both, as well as various combinations of major and minor over and under drilling errors. Five endodontists who had professional training and varying levels of experiences in root canal treatment participated as raters in the experiment.
RESULTS: Results from evaluation of our system with expert endodontists show a high degree of agreement with expert scores (information based measure of disagreement 0.04-0.21). At the same time they show some disagreement among human expert scores, reflecting the subjective nature of human outcome scoring. The discriminatory power of the AOS scores analyzed with three grade tiers (A, B, C) using the area under the receiver operating characteristic curve (AUC). The AUC values are generally highest for the {AB: C} cutoff which is cutoff at the boundary between clinically acceptable (B) and clinically unacceptable (C) grades.
CONCLUSIONS: The objective consistency of computed scores and high degree of agreement with experts make the proposed system a promising addition to existing VR simulators. The translation of detailed level scores into terminology commonly used in dental surgery supports natural communication with students and instructors. With the reference virtual templates created automatically, the approach is robust and is applicable in scoring the outcome of any dental surgery procedure involving the act of drilling.

Abstract
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This work presents the multilayered caries model with a visuo-tactile virtual reality simulator and a randomized controlled trial protocol to determine the effectiveness of the simulator in training for minimally invasive caries removal. A three-dimensional, multilayered caries model was reconstructed from 10 micro-computed tomography (CT) images of deeply carious extracted human teeth before and after caries removal. The full grey scale 0-255 yielded a median grey scale value of 0-9, 10-18, 19-25, 26-52, and 53-80 regarding dental pulp, infected carious dentin, affected carious dentin, normal dentin, and normal enamel, respectively. The simulator was connected to two haptic devices for a handpiece and mouth mirror. The visuo-tactile feedback during the operation varied depending on the grey scale. Sixth-year dental students underwent a pretraining assessment of caries removal on extracted teeth. The students were then randomly assigned to train on either the simulator (n=16) or conventional extracted teeth (n=16) for 3 days, after which the assessment was repeated. The posttraining performance of caries removal improved compared with pretraining in both groups (Wilcoxon, p<0.05). The equivalence test for proportional differences (two 1-sided t-tests) with a 0.2 margin confirmed that the participants in both groups had identical posttraining performance scores (95% CI=0.92, 1; p=0.00). In conclusion, training on the micro-CT multilayered caries model with the visuo-tactile virtual reality simulator and conventional extracted tooth had equivalent effects on improving performance of minimally invasive caries removal.

Abstract
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Targeted intervention and resource allocation are essential in effective control of infectious diseases, particularly those like malaria that tend to occur in remote areas. Disease prediction models can help support targeted intervention, particularly if they have fine spatial resolution. But there is typically a tradeoff between spatial resolution and predictability of the time series of infection. In this paper we present a systematic method to quantify the relationship between spatial resolution and predictability of disease and to help provide guidance in selection of appropriate spatial resolution. We introduce a complexity-based approach to spatial hierarchical clustering. We show that use of reduction in Akaike Information Criterion (AIC) as a clustering criterion leads to significantly more rapid improvement in predictability than spatial clustering alone. We evaluate our approach with two years of malaria case data from northern Thailand.

Abstract
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Situation awareness is known to be a critical skill in surgical decision making. While a few simulators have been developed to teach surgical decision making, none explicitly address teaching situation awareness skills. In this paper we present a knowledge representation framework that captures the key elements in reasoning about situation awareness. The framework makes use of concepts from AI planning and uses PDDL to represent surgical procedures. We describe tutorial feedback strategies identified in a preliminary observational study of endodontic surgery. We then present algorithms that implement these strategies using the knowledge representation framework. We show how the representation supports generating a number of tutorial interventions observed in teaching sessions by expert endodontic surgeons. We finally describe the contributions of our work.

Abstract
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This paper addresses the issue of the transatlantic gap in research excellence between Europe and USA by examining the performance of individual universities. It introduces a notion of leadership in research excellence by combining a subjective definition of excellence with an objective one. It applies this definition to a novel dataset disaggregated for 251 Subject Categories, covering the 2007-2010 period, based on Scopus data. The paper shows that European universities are able to show excellence only in a few disciplinary areas each, while US universities are able to excel across the board. It explains this difference in terms of institutional differences in recruitment process and governance of universities. It discusses the European model of distributed excellence in terms of the recent rise of input competition.

Abstract
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Disease outbreaks are important to predict since they indicate hot spots of transmission with high risk of spread to neighboring regions and can thus guide the allocation of resources. While numeric prediction models can be easily used for outbreak prediction by setting thresholds, an alternative is to build a model that specifically classifies situations into outbreak or none. In this paper we compare Bayesian network models built for the outbreak classification problem with Bayesian network, ARIMA and ARIMAX models built for numeric prediction and used for outbreak prediction by thresholding. We show that in most cases the classification models outperform the other models. We then investigate the reasons underlying the differences in performance among the models in order to shed light on their strengths and weaknesses. The models are developed and evaluated using two years of malaria and environmental data from northern Thailand.

Abstract
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In this paper we address the problem of classifying cited work into important and non-important to the developments presented in a research publication. This task is vital for the algorithmic techniques that detect and follow emerging research topics and to qualitatively measure the impact of publications in increasingly growing scholarly big data. We consider cited work as important to a publication if that work is used or extended in some way. If a reference is cited as background work or for the purpose of comparing results, the cited work is considered to be non-important. By employing five classification techniques (Support Vector Machine, Na๏ve Bayes, Decision Tree, K-Nearest Neighbors and Random Forest) on an annotated dataset of 465 citations, we explore the effectiveness of eight previously published features and six novel features (including context based, cue words based and textual based). Within this set, our new features are among the best performing. Using the Random Forest classifier we achieve an overall classification accuracy of 0.91 AUC.

Abstract
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Information and Communication Technology (ICT) is used to support developing countries in many different ways, such as poverty reduction, public services enhancement, and disaster management and recovery. Mobile4D is a software framework that applies the crowdsourcing paradigm to facilitate information exchange between people during disaster situations. It acts as a disaster reporting and alerting system as well as an information sharing platform. Mobile4D facilitates rapid communication between local citizens and administrative units. Moreover, it allows exchanging experience and knowledge between people to reduce poverty and increase living standards. The Mobile4D framework has been deployed in a pilot study in three provinces in the Lao People’s Democratic Republic (Lao PDR). The study was limited to report particular types of disasters, however, it revealed further use cases and identified the required extension of Mobile4D to cover the entire country. This paper presents a report about Mobile4D: initiative, challenges, status, and further extensions.

Abstract
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Since few universities can afford to be excellent in all subject areas, university administrators face the difficult decision of selecting areas for strategic investment. While the past decade has seen a proliferation of university ranking systems, several aspects in the design of most ranking systems make them inappropriate to benchmark performance in a way that supports formulation of effective institutional research strategy. To support strategic decision making, universities require research benchmarking data that is sufficiently fine-grained to show variation among specific research areas and identify focused areas of excellence; is objective and verifiable; and provides meaningful comparisons across the diversity of national higher education environments. This paper describes the Global Research Benchmarking System (GRBS) which satisfies these requirements by providing fine-grained objective data to internationally benchmark university research performance in over 250 areas of Science and Technology. We provide analyses of research performance at country and university levels, using the diversity of indicators in GRBS to examine distributions of research quality in countries and universities as well as to contrast university research performance from volume and quality perspectives. A comparison of the GRBS results with those of the three predominant ranking systems shows how GRBS is able to identify pockets of excellence within universities that are overlooked by the more traditional aggregate level approaches.

Abstract
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With the minute margins of error in endodontic surgery, training in manual dexterity and proper instrument handling are crucial components in the dental curriculum. Important parameters include tool path, tool angulation, and force applied. In this work, we focus on training of correct application of force. This is particularly challenging since the amounts of force used are on the order of tenths of Newtons, requiring a highly refined tactile sense and incorrect force can cause irreversible damage. Too great a force can cause overdrilling or in extreme cases perforation of the tooth. Too small a force can cause thermal irritation possibly resulting in tissue necrosis. Despite the importance of correct use of force, this is the dimension on which students receive the least tutorial feedback since force information is typically not available in traditional training settings. In this paper, we present an approach to using haptic feedback as a means to convey formative feedback on the correct application of force. Feedback is conveyed to the student graphically and the correct amount of force to apply is trained haptically. The simulator is rewound and the student is asked to redo the stage where the error occurred. Preliminary evaluation against a control group of students who received only feedback concerning outcome shows the feedback mechanism to be effective.

Abstract
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We present a virtual reality simulator for teaching emergency management decision-making in endodontic surgery. Objectives of the simulator are to 1) teach how to correctly respond to a variety of emergency situations, 2) acclimate students to making decisions in stressful emergency situations and 3) teach students the situation awareness skills required to rapidly recognize and respond to emergencies. To meet these objectives, we present a simulator that permits emergency situations to be dynamically inserted at various points in the procedure and that is immersive. The simulator also allows a teacher to observe and review a session in real-time or post session. Preliminary evaluation of face and content validity shows that the simulation is sufficiently realistic and the system is a promising teaching tool.

Abstract
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The paper exploits a newly created dataset offering several detailed bibliometric data and indicators on 251 subject categories for a large sample of universities in Europe, North America and Asia. In particular, it addresses the controversial issue of the distance between Europe and USA in research excellence (so called “transatlantic gap”). By building up indicators of objective excellence (top 10% worldwide in publications and citations) and subjective excellence (top 10% in the distribution of share of top journals out of total production at university level), it is shown that European universities fail to achieve objective measures of global excellence, while being competitive only in few fields. The policy implications of this state of affairs are discussed.

Abstract
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Since malaria is prevalent in less developed and more remote areas in which public health resources are often scarce, targeted intervention is essential in allocating resources for effective malaria control. To effectively support targeted intervention, predictive models must be not only accurate but they must also have high temporal and spatial resolution to help determine when and where to intervene. In this paper we take the first essential step towards a system to support targeted intervention in Thailand by developing a high resolution prediction model through the combination of Bayes nets and ARIMA. Bayes nets and ARIMA have complementary strengths, with the Bayes nets better able to represent the effect of environmental variables and ARIMA better able to capture the characteristics of the time series of malaria cases. Leveraging these complementary strengths, we develop an ensemble predictor from the two that has significantly better accuracy that either predictor alone. We build and test the models with data from Tha Song Yang district in northern Thailand, creating village-level models with weekly temporal resolution.

Abstract
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While a diversity of modeling technique have been used to create predictive models of malaria, no work has made use of Bayesian networks. Bayes nets are attractive due to their ability to represent uncertainty, model time lagged and nonlinear relations, and provide explanations of inferences. This paper explores the use of Bayesian networks to model malaria, demonstrating the approach by creating a village level model with weekly temporal resolution for Tha Song Yang district in northern Thailand. The network is learned using data on cases and environmental covariates. The network models incidence over time as well as evolution of the environmental variables, and captures time lagged and nonlinear effects. Out of sample evaluation shows the model to have high accuracy for one and two week predictions.

Abstract
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The academic and research policy communities have seen a long debate concerning themerits of peer review and quantitative citation-based metrics in evaluation of research.Some have called for replacing peer review with use of metrics for some evaluation pur-poses, while others have called for the use peer review informed by metrics. Whateverone’s position, a key question is the extent to which peer review and quantitative metricsagree. In this paper we study the relation between the three journal metrics source nor-malized impact per paper (SNIP), raw impact per paper (RIP) and Journal Impact Factor(JIF) and human expert judgement. Using the journal rating system produced by the Excel-lence in Research for Australia (ERA) exercise, we examine the relationship over a set ofmore than 10,000 journals categorized into 27 subject areas. We analyze the relationshipfrom the dimensions of correlation, distribution of the metrics over the rating tiers, andROC analysis. Our results show that SNIP consistently has stronger agreement with the ERArating, followed by RIP and then JIF along every dimension measured. The fact that SNIPhas a stronger agreement than RIP demonstrates clearly that the increase in agreement isdue to SNIP’s database citation potential normalization factor. Our results suggest that SNIPmay be a better choice than RIP or JIF in evaluation of journal quality in situations whereagreement with expert judgment is an important consideration.

Abstract
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Use of simulation to teach decision making in surgery is challenging partly due to the situated nature of the decisions, with situation awareness playing a critical role in making high quality decisions. Thus simulation systems need to be able to provide the key cues needed in making decisions with high fidelity. In this paper we present the first version of Desitra, a simulation environment for teaching decision making in dental surgery. System design was driven by an observational study of teaching sessions for endodontic surgery in the operating room which identified perceptual cues used in decision making as well as tutorial intervention strategies used by surgeons. Desitra provides an open environment for learning decision making – students carry out dental procedures and are free to make mistakes. The pedagogical module monitors the student actions and intervenes when students make mistakes, providing as little guidance as necessary to keep students on a productive learning path. The system is implemented to run on Android tablets to be maximally accessible. Preliminary evaluation of the system shows that Desitra effectively captures key perceptual cues.

Abstract
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Numerous VR simulators have been developed as a means of addressing limitations of the traditional apprenticeship approach to dental surgical skill training. Most existing simulators support intra- and extra-coronal procedures such as carries removal. In this paper we address the problem of automated outcome assessment for endodontic surgery. Outcome assessment is an essential component of any system that provides formative feedback, which requires assessing the outcome, relating it to the procedure, and communicating in a language natural to dental students. This paper takes a first step toward automated generation of such comprehensive feedback. Our system automatically computes reference templates based on tooth anatomy, which provides flexibility to adjust parameters such as tolerance and to create new templates on demand. Detailed scores are transformed into the standard scoring language used by dental schools. Preliminary evaluation of our system on fifteen outcome samples with three expert endodontists shows a high degree of agreement with expert scores.

Abstract
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Recognizing clinical style is essential for generating intelligent guidance in virtual reality simulators for dental skill acquisition. The aim of this study was to determine the potential of Dynamic Time Warping (DTW) in matching novices’ tooth cutting sequences with those of experts. Forty dental students and four expert dentists were enrolled to perform access opening to the root canals with a simulator. Four experts performed in manners that differed widely in the tooth preparation sequence. Forty students were randomly allocated into four groups and were trained following each expert. DTW was performed between each student’s sequence and all the expert sequences to determine the best match. Overall, the accuracy of the matching was high (95%). The current results suggest that the DTW is a useful technique to find the best matching expert for a student so that feedback based on that expert’s performance can be given to the novice in clinical skill training.

Abstract
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Crowdsensing can provide real time and detailed information about rapidly evolving crisis situations to facilitate rapid response and effective resource allocation. But while challenges such as heterogeneity of data content and quality, asynchronicity, and volume call for robust data integration and interpretation capabilities, situation awareness in crowdsensing for crisis management remains a largely unexplored area of research. In this paper we extend the mobile4D smartphone-based disaster reporting and alerting system with a situation awareness data interpretation and integration layer and demonstrate its application to the problem of tracking cholera outbreaks. The communication workflow in mobile4D-SA supports interaction between crowdsensed information, system predictions, and multifaceted communication between authorities and affected people on the ground.

Abstract
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The traditional apprenticeship approach to dental surgical skill training has known limitations including subjectivity of evaluation, scarcity of available experts, and lack of standardization. As an attempt to address these limitations, dentistry schools have begun to incorporate virtual reality (VR) simulators into surgical curricula. However, automated outcome scoring is not fully supported in existing dental VR simulators. Without automatic outcome analysis, students must still depend on human experts for evaluation of the outcome. With the limited availability of expert supervision, students often end up in unsupervised training with delayed feedback. In this study, we present an approach to automate the process of outcome scoring in dental simulators. Automated outcome scoring is an initial step toward our larger endeavor of automated objective assessment and real-time feedback generation for surgical skill training.

Abstract
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Most surgical simulations focus on enhancing the learning of technical skill; whereas, teaching of non-technical skills particularly decision making skills has received significantly less attention. While some computer-based system for teaching decision making have been developed, they lack the richness of interaction that occurs between student and expert in the operating room. With the end objective of developing an automated system to teach decision making skills, this paper takes a first step by carrying out an observational study of expert tutorial interventions in teaching intraoperative decision making in dental surgery. Actions and discussions were transcribed. Decisions made by novice, assistant, and interventions by expert were identified. The expert interventions were clustered into types. The situation triggering each intervention type was determined. Preliminary analysis of expert interventions identified seven types of expert intervention strategies, in which five of them were found for teaching decision making. The analysis also identified the triggering situation of each, intervention strategies usage comparison in teaching technical and decision making skill, as well as the interaction patterns of among expert, novice, and assistant. The results provide a foundation for designing the pedagogical strategies for an intelligent tutoring system for decision making in dental surgery.

In this paper we propose a new technique to semantically analyze knowledge flows across countries by using publication and citation data. We start with the identification of research topics produced by a given source country. Then, we collect papers, published by the authors outside the source country, citing the identified research topics. At last, we group each set of citing papers separately to determine the scholarly impact of the actual identified research topics in the cited topics. The research topics are identified using our proposed topic model with distance matrix, an extension of classic Latent Dirichlet Allocation model. We also present a case study to illustrate the use of our proposed techniques in the subject area Energy during 2004–2009 using the Scopus database. We compare the Japanese and Chinese papers that cite the scientific literature produced by the researchers from the United States in order to show the difference in the use of same knowledge. The results indicate that Japanese researchers focus in the research areas such as efficient use of Photovoltaic, Energy Conversion and Superconductors (to produce low-cost renewable energy). In contrast with the Japanese researchers, Chinese researchers focus in the areas of Power Systems, Power Grids and Solar Cells production. Such analyses are useful for understanding the dynamics of the relevant knowledge flows across the nations.

N/A

Abstract
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This paper presents a bibliometric study of the world's research activity in Sustainable Development using scientific literature. The study was conducted using data from the Scopus database over the time period of 2000–2010. We investigated the research landscape in Sustainable Development at country level and at institute level. Sustainable Development and its sub-areas are defined by keywords vetted by the domain experts, allowing publications to be identified independent of the journals and conferences in which they are published. The results indicate that institutes strong in Sustainable Development overall may not be strong in all sub-areas and that institutes not strong in Sustainable Development overall may have significant niche strengths in a given sub-area. It is also noted that China appears strong in terms of publication output in Sustainable Development and its sub-areas but it does not appear strong in terms of citation counts. The information produced in this study can be useful for government research agencies in terms of understanding how to more effectively knit together the various niche strengths in the country; and for the institutes to find strategic partners that can coordinate in niche areas of Sustainable Development and complement their strengths. In order to conduct bibliometric analysis in an interdisciplinary research area, the keyword collection approach appears to be very useful. This approach is flexible and can be used to conduct such analysis for interdisciplinary research fields.

Abstract
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The knowledge acquisition bottleneck is a problem pertinent to the authoring of any intelligent tutoring system. Allowing students a broad scope of reasoning and solution representation whereby a wide range of plausible student solutions are accepted by the system, places additional burden on knowledge acquisition. In this paper we present a strategy to alleviate the burden of knowledge acquisition for building a tutoring system for medical problem-based learning (PBL). The Unified Medical Language System (UMLS) is deployed as domain ontology and information structure in the ontology is exploited to make intelligent inferences and expand the domain model. Using these inferences and expanded domain model, the tutoring system is able to accept a broader range of plausible student solutions that lie beyond the scope of explicitly encoded solutions. We describe the development of a tutoring system prototype and report the evaluation of system correctness in accepting such plausible solutions. The system evaluation indicates an average accuracy of 94.59 % when compared against human domain experts, who agreed among themselves with a statistical agreement based on Pearson Correlation Coefficient of 0.48 and p < 0.05.

Abstract
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In this paper, we describe the design and use of a knowledge sharing network that has recently been deployed for agricultural extension work in the Lao People's Democratic Republic (Lao PDR). The system, Poverty Reduction and Agricultural Management -- Knowledge Sharing Network (PRAM-KSN), was built using a collaborative design process that involved both experts and ministerial agricultural extension workers who are also the current users of this web-based platform. This paper also discusses the relevance of the PRAM-KSN for agricultural extension work, how the principles of crowdsourcing apply to the system, and how social learning occurs for the benefit of agricultural extension work. Suggestions for impact assessment of the PRAM-KSN at different time-frames are offered.

Abstract
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Based on the network comprised of 111,444 keywords of library and information science that are extracted from Scopus, and taken into consideration the major properties of average distance and clustering coefficients, the present authors, with the knowledge of complex network and by means of calculation, reveal the small-world effect of the keywords network. On the basis of the keywords network, the betweenness centrality is used to carry out a preliminary study on how to detect the research hotspots of a discipline. This method is also compared with that of detecting research hotspots by word frequency.

Abstract
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Problem based learning is becoming widely popular as an effective teaching method in medical education. Paying individual attention to a small group of students in medical problem-based learning (PBL) can place burden on the workload of medical faculty whose time is very costly. Intelligent tutoring systems offer a cost effective alternative in helping to train the students, but they are typically prone to brittleness and the knowledge acquisition bottleneck. Existing tutoring systems accept a small set of approved solutions for each problem scenario stored into the system. Plausible student solutions that lie outside the scope of the explicitly encoded ones receive little acknowledgment from the system. Tutoring hints are also confined to the knowledge space of the approved solutions, leading to brittleness in the tutoring approach. We report the clinical reasoning gains off a tutoring system for medical PBL that employs and represents the widely available medical knowledge source UMLS as the domain ontology. We exploit the structure of the concept hierarchy to expand the plausible solution space and generate hints based on the problem solving context. Evaluation of student learning outcomes led to highly significant learning gains (Mann-Whitney, p < 0.001).

Abstract
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We introduce a new quantitative measure of international scholarly impact of countries by using bibliometric techniques based on publication and citation data. We present a case study to illustrate the use of our proposed measure in the subject area Energy during 1996---2009. We also present geographical maps to visualize knowledge flows among countries. Finally, using correlation analysis between publication output and international scholarly impact, we study the explanatory power of the applied measure.

Abstract
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Two relevant recent developments in the area of science and technology (S&T) and related policy-making motivate this article: first, bibliometric data on a specific research area's performance becomes an increasingly relevant source for S&T policy making and evaluation. This trend is embedded in wider discussions on evidence-based policy-making. Secondly, the scientific output of Southeast Asian countries is rising, as is the number of international research collaborations with the second area of our interest: Europe. Against this background, we employ basic bibliometric methodology in order to draw a picture of Southeast Asian research strengths as well the amount and focus of S&T cooperation between the countries in Southeast Asia and the European Union. The results can prove useful for an interested public as well as for the scientific community and science, technology and innovation policy-making.

Abstract
========
While problem-based learning has become widely popular for imparting clinical reasoning skills, the dynamics of medical PBL require close attention to a small group of students, placing a burden on medical faculty, whose time is over taxed. Intelligent tutoring systems (ITSs) offer an attractive means to increase the amount of facilitated PBL training the students receive. But typical intelligent tutoring system architectures make use of a domain model that provides a limited set of approved solutions to problems presented to students. Student solutions that do not match the approved ones, but are otherwise partially correct, receive little acknowledgement as feedback, stifling broader reasoning. Allowing students to creatively explore the space of possible solutions is exactly one of the attractive features of PBL. This paper provides an alternative to the traditional ITS architecture by using a hint generation strategy that leverages a domain ontology to provide effective feedback. The concept hierarchy and co-occurrence between concepts in the domain ontology are drawn upon to ascertain partial correctness of a solution and guide student reasoning towards a correct solution. We describe the strategy incorporated in METEOR, a tutoring system for medical PBL, wherein the widely available UMLS is deployed and represented as the domain ontology. Evaluation of expert agreement with system generated hints on a 5-point likert scale resulted in an average score of 4.44 (Spearman's ? = 0.80, p < 0.01). Hints containing partial correctness feedback scored significantly higher than those without it (Mann Whitney, p < 0.001). Hints produced by a human expert received an average score of 4.2 (Spearman's ? = 0.80, p < 0.01).

Abstract
=======
Aim To design and evaluate the impact of virtual reality (VR) pre-surgical practice on the performance of actual endodontic microsurgery.
Methodology
=======
The VR system operates on a laptop with a 1.6-GHz Intel processor and 2 GB of main memory. Volumetric cone-beam computed tomography (CBCT) data were acquired from a fresh cadaveric porcine mandible prior to endodontic microsurgery. Ten inexperienced endodontic trainees were randomized as to whether they performed endodontic microsurgery with or without virtual pre-surgical practice. The VR simulator has microinstruments to perform surgical procedures under magnification. After the initial endodontic microsurgery, all participants served as their own controls by performing another procedure with or without virtual pre-surgical practice. All procedures were videotaped and assessed by two independent observers using an endodontic competency rating scale (from 6 to 30).
Results
=======
A significant difference was observed between the scores for endodontic microsurgery on molar teeth completed with virtual pre-surgical practice and those completed without virtual presurgical practice, median 24.5 (range = 17– 28) versus median 18.75 (range = 14–26.5), P = 0.041. A significant difference was observed between the scores for osteotomy on a molar tooth completed with virtual pre-surgical practice and those completed without virtual pre- surgical practice, median 4.5 (range = 3.5–4.5) versus median 3 (range = 2–4), P = 0.042.
Conclusions
=======
Pre-surgical practice in a virtual environment using the 3D computerized model generated from the original CBCT image data improved endodontic microsurgery performance.

Abstract
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This paper presents a bibliometric study of the world's research activity in Sustainable Development using scientific literature. The study was conducted using data from the Scopus database over the time period of 2000-2010. We investigated the research landscape in Sustainable Development at country level and at institute level. Sustainable Development and its sub-areas are defined by keywords vetted by the domain experts, allowing publications to be identified independent of the journals and conferences in which they are published. The results indicate that institutes strong in Sustainable Development overall may not be strong in all sub-areas and that institutes not strong in Sustainable Development overall may have significant niche strengths in a given sub-area. It is also noted that China appears strong in terms of publication output in Sustainable Development and its sub-areas but it does not appear strong in terms of citation counts. The information produced in this study can be useful for government research agencies in terms of understanding how to more effectively knit together the various niche strengths in the country; and for the institutes to find strategic partners that can coordinate in niche areas of Sustainable Development and complement their strengths. In order to conduct bibliometric analysis in an interdisciplinary research area, the keyword collection approach appears to be very useful. This approach is flexible and can be used to conduct such analysis for interdisciplinary research fields.

Abstract
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Problem based learning is becoming widely popular as an effective teaching method in medical education. Paying individual attention to a small group of students in medical PBL can place burden on the workload of medical faculty whose time is very costly. Intelligent tutoring systems offer a cost effective alternative in helping to train the students, but they are typically prone to brittleness and the knowledge acquisition bottleneck. Existing tutoring systems accept a small set of approved solutions for each problem scenario stored into the system. Plausible student solutions that lie outside the scope of the explicitly encoded ones receive little acknowledgment from the system. Tutoring hints are also confined to the knowledge space of the approved solutions, leading to brittleness in the tutoring approach. We report a tutoring system for medical PBL that employs the widely available medical knowledge source UMLS as the domain ontology. We exploit the structure of the ontology to expand the plausible solution space and generate hints based on the problem solving context. Evaluation of student learning outcomes led to highly significant learning gains (Mann-Whitney, p<0.001).

Abstract
========
Objective
========
We present a dental training simulator that provides a virtual reality (VR) environment with haptic feedback for dental students to practice dental surgical skills in the context of a crown preparation procedure. The simulator addresses challenges in traditional training such as the subjective nature of surgical skill assessment and the limited availability of expert supervision.
Methods and materials
=================
We identified important features for characterizing the quality of a procedure based on interviews with experienced dentists. The features are patterns combining tool position, tool orientation, and applied force. The simulator monitors these features during the procedure, objectively assesses the quality of the performed procedure using hidden Markov models (HMMs), and provides objective feedback on the user's performance in each stage of the procedure. We recruited five dental students and five experienced dentists to evaluate the accuracy of our skill assessment method and the quality of the system's generated feedback.
Results
========
The experimental results show that HMMs with selected features can correctly classify all test sequences into novice and expert categories. The evaluation also indicates a high acceptance rate from experts for the system's generated feedback.
Conclusion
========
In this work, we introduce our VR dental training simulator and describe a mechanism for providing objective skill assessment and feedback. The HMM is demonstrated as an effective tool for classifying a particular operator as novice-level or expert-level. The simulator can generate tutoring feedback with quality comparable to the feedback provided by human tutors.

Abstract
=======
AIM:
======
To evaluate the effectiveness of haptic virtual reality (VR) simulator training using microcomputed tomography (micro-CT) tooth models on minimizing procedural errors in endodontic access preparation.
METHODOLOGY:
==========
Fourth year dental students underwent a pre-training assessment of access cavity preparation on an extracted maxillary molar tooth mounted on a phantom head. Students were then randomized to training on either the micro-CT tooth models with a haptic VR simulator (n = 16) or extracted teeth in a phantom head (n = 16) training environments for 3 days, after which the assessment was repeated. The main outcome measure was procedural errors assessed by an expert blinded to trainee and training status. The secondary outcome measures were tooth mass loss and task completion time. The Wilcoxon test was used to examine the differences between pre-training and post-training error scores, on the same group. The Mann-Whitney test was used to detect any differences between haptic VR training and phantom head training groups. The independent t-test was used to make a comparison on tooth mass removed and task completion time between the haptic VR training and phantom head training groups.
RESULTS:
=======
Post-training performance had improved compared with pre-training performance in error scores in both groups (P < 0.05). However, error score reduction between the haptic VR simulator and the conventional training group was not significantly different (P > 0.05). The VR simulator group decreased significantly (P < 0.05) the amount of hard tissue volume lost on the post-training exercise. Task completion time was not significantly different (P > 0.05) in both groups.
CONCLUSIONS:
==========
Training on the haptic VR simulator and conventional phantom head had equivalent effects on minimizing procedural errors in endodontic access cavity preparation.

Abstract
========
We have developed a virtual reality (VR) and an augmented reality (AR) dental training simulator utilizing a haptic device. The simulators utilize volumetric force feedback computation and real time modification of the volumetric data. They include a virtual mirror to facilitate indirect vision during a simulated operation. The AR environment allows students to practice surgery in correct postures by combining the 3D tooth and tool models with the realworld view and displaying the result through a video see-through head-mounted display (HMD). Preliminary results from an initial evaluation show that the system is a promising tool to supplement dental training and that there are advantages of the AR over the VR approach.

Abstract
========
Tutoring systems typically contain or generate a set of approved solutions to problems presented to students. Student solutions that don't match the approved ones, but are otherwise partially correct, receive little acknowledgement as feedback, stifling broader reasoning. Additionally, feedback mechanisms rely on having the student model, which requires extensive effort to build. This paper provides an alternative to the traditional ITS architecture by using a hint generation strategy that bypasses the student model and instead leverages off of the domain ontology. Concept hierarchy and co-occurrence between concepts in the domain ontology are drawn upon to ascertain partial correctness of a solution and guide student reasoning towards the correct solution. We describe the strategy incorporated in a tutoring system for medical PBL, wherein the widely available UMLS is deployed as the domain ontology. Evaluation of expert agreement with system generated hints on a 5-point likert scale resulted in an average score of 4.44 (r = 0.9018, p < 0.05). Hints containing partial correctness feedback scored significantly higher than those without it (Wilcoxon Rank Sum, p < 0.001)

N/A

Abstract
========
We have developed a haptic virtual reality system for dental skill training. In this study we examined several kinds of kinematic information about the movement provided by the system supplement knowledge of results (KR) in dental skill acquisition. The kinematic variables examined involved force utilization (F) and mirror view (M). This created three experimental conditions that received augmented kinematic feedback (F, M, FM) and one control condition that did not (KR-only). Thirty-two dental students were randomly assigned to four groups. Their task was to perform access opening on the upper first molar with the haptic virtual reality system. An acquisition session consisted of two days of ten trials of practice in which augmented kinematic feedback was provided for the appropriate experimental conditions after each trial. One week after, a retention test consisting of two trials without augmented feedback was completed. The results showed that the augmented kinematic feedback groups had larger mean performance scores than the KR-only group in Day 1 of the acquisition and retention sessions (ANOVA, p<0.05). The apparent differences among feedback groups were not significant in Day 2 of the acquisition session (ANOVA, p>0.05). The trends in acquisition and retention sessions suggest that the augmented kinematic feedback can enhance the performance earlier in the skill acquisition and retention sessions.

Abstract
========
OBJECTIVES: We present a dental training system with a haptic interface that allows dental students or experts to practice dental procedures in a virtual environment. The simulator is able to monitor and classify the performance of an operator into novice or expert categories. The intelligent training module allows a student to simultaneously and proactively follow the correct dental procedures demonstrated by an intelligent tutor.
METHODS:
========
The virtual reality (VR) simulator simulates the tooth preparation procedure both graphically and haptically, using a video display and haptic device. We evaluated the performance of users using hidden Markov models (HMMs) incorporating various data collected by the simulator. We implemented an intelligent training module which is able to record and replay the procedure that was performed by an expert and allows students to follow the correct steps and apply force proactively by themselves while reproducing the procedure.
RESULTS:
========
We find that the level of graphics and haptics fidelity is acceptable as evaluated by dentists. The accuracy of the objective performance assessment using HMMs is encouraging with 100 percent accuracy.
CONCLUSIONS:
========
The simulator can simulate realistic tooth surface exploration and cutting. The accuracy of automatic performance assessment system using HMMs is also acceptable on relatively small data sets. The intelligent training allows skill transfer in a proactive manner which is an advantage over the passive method in a traditional training. We will soon conduct experiments with more participants and implement a variety of training strategies.

Abstract
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Introduction Haptic virtual reality (VR) has revolutionized the skill acquisition in dentistry. The strength of the haptic VR system is that it can automatically record the outcome and associated kinematic data on how each step of the task is performed, which are not available in the conventional skill training environments. The aim of this study was to assess skill acquisition in endodontics and to identify process and outcome variables for the quantification of proficiency.
Methods
========
Twenty novices engaged in the experimental study that involved practicing the access opening task with the haptic VR system. Process (speed, force utilization, and bimanual coordination) and outcome variables were determined for assessing skill performance. These values were compared before and after training.
Results
========
Significant improvements were observed through training in all variables. A unique force used pattern and bimanual coordination were observed in each step of the access opening in the posttraining session. The novices also performed the tasks considerably faster with greater outcome within the first two to three training sessions.
Conclusions
========
The study objectively showed that the novices could learn to perform access opening tasks faster and with more consistency, better bimanual dexterity, and better force utilization. The variables examined showed great promise as objective indicators of proficiency and skill acquisition in haptic VR.

Abstract
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Note taking is a common way for physicians to collect information from their patients in medical inquiries and diagnoses. Many times, when describing the pathology in medical records, a physician also draws diagrams and/or anatomical sketches along with the free-text narratives. The ability to understand unstructured handwritten texts and drawings in patient record could lead to implementation of automated patient record systems with more natural interfaces than current highly structured systems. The first and crucial step in automated processing of free-hand medical records is to segment the record into handwritten text and drawings, so that appropriate recognizers can be applied to different regions. This paper presents novel algorithms that separate text from non-text strokes in an on-line handwritten patient record. The algorithm is based on analyses of spatio-temporal graphs extracted from an on-line patient record and support vector machine (SVM) classification. Experiments demonstrate that the proposed approach is effective and robust.

Abstract
========
Dental students devote several years to the acquisition of sufficient psychomotor skills to prepare them for entry-level dental practice. Traditional methods of dental surgical skills training and assessment are being challenged by the complications such as the lack of real-world cases, unavailability of expert supervision and the subjective manner of surgical skills assessment. To overcome these challenges, we developed a dental training system that provides a VR environment with a haptic device for dental students to practice tooth preparation procedures. The system monitors important features of the procedure, objectively assesses the quality of the performed procedure using hidden Markov models, and provides objective feedback on the user's performance for each stage in the procedure. Important features for characterizing the quality of the procedure were identified based on interviews with experienced dentists. We evaluated the accuracy of the skill assessment with data collected from novice dental students as well as experienced dentists. We also evaluated the quality of the system's feedback by asking a dental expert for comments. The experimental results show high accuracy in classifying users into novice and expert, and the evaluation indicated a high acceptance rate for the generated feedback.

Abstract
========
Traditional methods of dental surgical skills training and assessment are being challenged by complications such as unavailability of expert supervision and the subjective manner of surgical skills assessment. This paper presents a dental surgical skills training system that provides a virtual reality environment with a haptic device for dental students to practice tooth preparation procedures. The system monitors important features of the procedures, objectively assesses the quality of the performed procedure and provides objective feedback on the user's performance for each stage in the procedure. We evaluated the accuracy of the skill assessment with data collected from novice dental students as well as experienced dentists. The experimental results show high accuracy in classifying users into novice and expert. The evaluation of the system's generated feedback also indicated a high acceptance rate.

Abstract
========
In well-defined domains such as Physics, Mathematics, and Chemistry, solutions to a posed problem can objectively be classified as correct or incorrect. In ill-defined domains such as medicine, the classification of solutions to a patient problem as correct or incorrect is much more complex. Typical tutoring systems accept only a small set of approved solutions for each problem scenario fed to the system. Plausible student solutions that fall outside the scope of this small set of approved solutions are rejected as being incorrect, even though these solutions may be acceptable or close to acceptable. This leads to brittleness in the evaluation of student solutions. This paper describes a tutoring system for medical problem-based learning (PBL), which can accept a wide variety of plausible solutions without placing an extensive burden on knowledge acquisition. A widely available medical knowledge source is deployed as a domain ontology, and concept relationships in the ontology are used to make inferences and expand the space of plausible solutions beyond the scope of solutions explicitly provided to the system. Parent-child relationships are used to infer generalized solutions, whereas relationships of synonymy are used to infer alternate solutions. Evaluations of the system after expanding the solution space indicate accuracy close to that of human experts, who agreed among themselves with Pearson Correlation Coefficient of 0.48 and p < 0.05. The system precision dropped by 32%, while the recall increased by five times. The geometric mean of sensitivity and specificity was increased by 0.33.

Abstract
========
OBJECTIVES:
========
The goal of dental education is to guide students' development through different stages from novice to competent, eventually resulting in an expert clinician. In this study we sought to identify process and outcome measures of clinical skill performance by comparing novices and experts using a virtual reality (VR) simulation system developed by our group.
METHODS:
========
Ten novices (fourth-year dental students), and ten experts in prosthodontics performed a crown preparation task with a haptic VR that provided force feedback to the operating tool while interacting with the virtual tissue/organ. For each step of the crown preparation, the system automatically recorded data associated with performance process including time to task completion (T), force used (F), and angulations (A) of the bur. The preparation outcome (O) scores were graded by an expert in the field. An independent t-test was conducted on all dependent variables (F in x-, y-, z-axes; A in zy, zx, xy planes; T and O) between experts and novices.
RESULTS:
========
Experts performed significantly better than novices (p<0.05) as shown by greater O. Expert T was significantly less than that of novices (p<0.05). Instrument A as well as F used were significantly different in almost all preparation steps in both groups (p<0.05).
CONCLUSION:
========
This study clearly demonstrated the ability of outcome and process measures to distinguish between novice and expert performance in crown preparation using a haptic VR system.

Abstract
========
The knowledge acquisition bottleneck is a problem pertinent to the authoring of any intelligent tutoring system. Allowing students a broad scope of reasoning and solution representation whereby a wide range of plausible student solutions are accepted by the system, places additional burden on knowledge acquisition. In this paper we present a strategy to alleviate the burden of knowledge acquisition for building a tutoring system for medical problem-based learning (PBL). The Unified Medical Language System (UMLS) is deployed as domain ontology and information structure in the ontology is exploited to make intelligent inferences and expand the domain model. Using these inferences and expanded domain model, the tutoring system is able to accept a broader range of plausible student solutions that lie beyond the scope of explicitly encoded solutions. We describe the development of a tutoring system prototype and report the evaluation of system correctness in accepting such plausible solutions. The system evaluation indicates an average accuracy of 94.59 % when compared against human domain experts, who agreed among themselves with a statistical agreement based on Pearson Correlation Coefficient of 0.48 and p < 0.05.

Abstract
========
In this paper, we present a novel collaborative authoring tool that was designed to allow medical teachers to formalize and visualize their knowledge for medical intelligent tutoring systems. Our goal is to increase the efficiency and effectiveness in creating the domain model representing the problem solution—often referred to as the bottleneck in developing intelligent tutoring systems. We incorporate the Unified Medical Language System (UMLS) knowledge base to assist the authors in creating the problem solution collaboratively via a video conferencing platform. The system consists of a shared workspace gathering information visualization and tools necessary for collaborative problem-solving tasks. We found that the authoring tool can be used to effectively elicit the knowledge structure of the domain model. This was achieved in hours compared to months for the conventional paper-based approach.

Abstract
========
Intelligent tutoring systems are no different from other knowledge based systems in that they are often plagued by brittleness. Intelligent tutoring systems for problem solving are typically loaded with problem scenarios for which specific solutions are constructed. Solutions presented by students, are compared against these specific solutions, which often leads to a narrow scope of reasoning, where students are confined to reason towards a specific solution. Student solutions that are different from the specific solution entertained by the system are rejected as being incorrect, even though they may be acceptable or close to acceptable. This leads to brittleness in tutoring systems in evaluating student solutions and returning appropriate feedback. In this paper we discuss a few humanlike attributes in the context of robustness that are desirable in knowledge based systems. We then present a model of reasoning through which a tutoring system for medical problem-based learning, can begin to exhibit human-like robust behavior in evaluating solutions in a broader context using UMLS, and respond with hints that are mindful of the partial correctness of the student solution.

Abstract
========
This paper presents a novel approach to deriving probabilistic models that predict enrollment given applicant background and the amount of financial aid offered. Our Bayesian network models can be used to optimize various enrollment objectives. We present a novel efficient optimization algorithm that uses the models to maximize expected tuition revenue under capacity constraints including student-faculty ratio and accommodation. We demonstrate and evaluate our approach using four years of graduate admissions data from the Asian Institute of Technology, consisting of 7,788 applicants from 84 different countries. This data set is particularly challenging since reliable family income data is not available for students from most of these countries. Evaluating the Bayesian network model with 10-fold cross validation yields an ROC Az value of 0.8451, with a predictive accuracy of 82.70% at a threshold of 0.5. Comparing the results of the tuition revenue optimization model to the institute's current financial aid allocation practice shows that if single-term tuition revenue is the sole optimization criterion, the institute can achieve its current enrollment numbers while realizing significant savings in its financial aid budget. The prediction and optimization software is currently being incorporated into the institute's online admissions processing system.

Abstract
========
In today's transnational admission environment, evaluating applicant qualifications is becoming increasingly challenging. While standardized tests can be helpful, studies have shown that they are rather noisy predictors of performance. Predicting educational outcome is a viable alternative in such heterogeneous environments. Performance prediction models can be built by applying data mining techniques to enrollment data. In this paper we present an approach to using Bayesian networks to predict graduating cumulative Grade Point Average based on applicant background at the time of admission. While such prediction models can be helpful, their recommendations may not be followed by departmental faculty members making admission decisions if they are presented as black boxes. We thus present a novel approach to deriving a case-based retrieval mechanism from the Bayesian network prediction model in such a way that the similarity measure used by the casebased system is consistent with the predictive model. The case-based component retrieves the past student most similar to the applicant being evaluated. The Bayesian network model is evaluated using stratified ten-fold cross validation.

Abstract
========
Effectively selling products online is a challenging task. Today's product domains often contain a dizzying variety of brands and models with highly complex sets of characteristics. This paper addresses the problem of supporting product search and selection in domains containing large numbers of alternatives with complex sets of features. A number of online shopping websites provide product choice assistance by making direct use of Multi-Attribute Utility Theory (MAUT). While the MAUT approach is appealing due to its solid theoretical foundations, there are several reasons that it does not fit well with people's decision making behavior. This paper presents an approach designed to better fit with people's natural decision making process. The system is called VMAP for Visualizing Multi-Attribute Preferences. VMAP provides on one screen both a multi-attribute preference tool (MAP-tool) and a product visualization tool (V-tool). The product visualization tool displays the set of available products, with each product displayed as a point in a 3D attribute space. By viewing the product space, users can gain an overview of the range of available products, as well as an understanding of the relationships between their attributes. The MAP-tool integrates expression of preferences and filter conditions, which are then immediately reflected in the V-tool display. In this way, the user can immediately see the consequences of his expressed preferences on the product space. The VMAP system is evaluated on a number of factors by comparing users' subjective ratings of the system to those of a more traditional MAUT product selection tool. The results show that while VMAP is somewhat more difficult to use than a traditional MAUT product selection tool, it provides better flexibility, provides the ability to more effectively explore the product domain, and produces more confidence in the selected product.

Abstract
========
This paper discussed about the developed collaborative intelligent tutoring system for medical PBL called Comet (collaborative medical tutor). Comet uses Bayesian networks to model the knowledge and activity of individual students as well as small groups. It applies generic tutoring algorithms to these models and generates tutorial hints that guide problem solving. An early laboratory study shows a high degree of agreement between the hints generated by Comet and those of experienced human tutors. Evaluations of Comet's clinical-reasoning model and the group reasoning path provide encouraging support for the general framework.

Abstract
========
Sketching is ubiquitous in medicine. Physicians commonly use sketches as part of their note taking in patient records and to help convey diagnoses and treatments to patients. Medical students frequently use sketches to help them think through clinical problems in individual and group problem solving. Applications ranging from automated patient records to medical education software could benefit greatly from the richer and more natural interfaces that would be enabled by the ability to understand sketches. In this paper we take the first steps toward developing a system that can understand anatomical sketches. Understanding an anatomical sketch requires the ability to recognize what anatomical structure has been sketched and from what view (e.g. parietal view of the brain), as well as to identify the anatomical parts and their locations in the sketch (e.g. parts of the brain), even if they have not been explicitly drawn. We present novel algorithms for sketch recognition and for part identification. We evaluate the accuracy of the recognition algorithm on sketches obtained from medical students. We evaluate the part identification algorithm by comparing its results to the judgment of an experienced physician.

Abstract
========
The study aims to develop a geographical information system (GIS) for surveillance of Aedes aegypti and dengue haemorrhagic fever (DHF) in north-eastern Thailand. There are three steps in the development of the GIS – collecting primary and secondary data, analysing the data and searching the target location, and presenting the results via figures on a map. Two sub-districts in each of the five districts in Ubon Ratchathani province with high incidences of DHF cases in the last three years were investigated. Primary {e.g. House Index (HI), Container Index (CI) and Breteau Index (BI)} and secondary data (e.g. number of DHF cases/100 000 population) were collected. The time period was divided into two phases, a low disease incidence phase (February–March 2007) and a high disease incidence phase (June–July 2007). The GIS was developed via ArcView programme 3.2a?. The primary data of Ae. aegypti indices including HI, CI and BI indicated a rise in the rainy season period compared with the dry weather period, and the secondary data showed a similar rise and fall in the number of DHF cases in the rainy and dry weather periods respectively. GIS technology can help in planning, implementation and evaluation of the dengue control measures.

Abstract
========
OBJECTIVE:
========
Evaluate the effectiveness of a computer-based program (CBL) introduced to improve the clinical and patient history taking skills of clinical pharmacists in the area of diabetes management. This program involved a self-learning approach utilizing interactive digital videos, video simulations, and audio clips.
MATERIAL AND METHOD:
========
The present study compared the pre- and post-test results of two groups of final year pharmacy students. The study group used the CBL program and the control group was exposed to formal lectures and discussions.
RESULTS:
=======
Eighty-three volunteers entered the present study. Forty-three were constituted into the study group, and forty acted as the control group. The overall results showed that the study group post-test scores in all basic knowledge areas were significantly higher than the control group (p = 0.001). Whereas, there was no statistical difference between groups in patient history taking skills (p = 0.645). Nevertheless, the post-test scores of SOAP writing skills in the study group were statistically higher than the control group (p = 0.001).
CONCLUSION:
========
Overall, the CBL program was considered effective in the development of basic knowledge of diabetes and in the improvement of patient history taking skills.

Abstract
========
Since problem solving in group problem-based learning is a collaborative process, modeling individuals and the group is necessary if we wish to develop an intelligent tutoring system that can do things like focus the group discussion, promote collaboration, or suggest peer helpers. We have used Bayesian networks to model individual student knowledge and activity, as well as that of the group. The validity of the approach has been tested with student models in the areas of head injury, stroke and heart attack. Receiver operating characteristic (ROC) curve analysis shows that, the models are highly accurate in predicting individual student actions. Comparison with human tutors shows that group activity determined by the model agrees with that suggested by the majority of the human tutors with a high degree of statistical agreement (McNemar test, p = 0.774, Kappa = 0.823).

Abstract
========
OBJECTIVES:
========
Today a great many medical schools have turned to a problem-based learning (PBL) approach to teaching. While PBL has many strengths, effective PBL requires the tutor to provide a high degree of personal attention to the students, which is difficult in the current academic environment of increasing demands on faculty time. This paper describes intelligent tutoring in a collaborative medical tutor for PBL. The main contribution of our work is the development of representational techniques and algorithms for generating tutoring hints in PBL group problem solving, as well as the implementation of these techniques in a collaborative intelligent tutoring system, COMET. The system combines concepts from computer-supported collaborative learning with those from intelligent tutoring systems.
METHODS AND MATERIALS:
==================
The system uses Bayesian networks to model individual student clinical reasoning, as well as that of the group. The prototype system incorporates substantial domain knowledge in the areas of head injury, stroke and heart attack. Tutoring in PBL is particularly challenging since the tutor should provide as little guidance as possible while at the same time not allowing the students to get lost. From studies of PBL sessions at a local medical school, we have identified and implemented eight commonly used hinting strategies. In order to evaluate the appropriateness and quality of the hints generated by our system, we compared the tutoring hints generated by COMET with those of experienced human tutors. We also compared the focus of group activity chosen by COMET with that chosen by human tutors.
RESULTS:
========
On average, 74.17% of the human tutors used the same hint as COMET. The most similar human tutor agreed with COMET 83% of the time and the least similar tutor agreed 62% of the time. Our results show that COMET's hints agree with the hints of the majority of the human tutors with a high degree of statistical agreement (McNemar test, p=0.652, kappa=0.773). The focus of group activity chosen by COMET agrees with that chosen by the majority of the human tutors with a high degree of statistical agreement (McNemar test, p=0.774, kappa=0.823).
CONCLUSION:
========
Bayesian network clinical reasoning models can be combined with generic tutoring strategies to successfully emulate human tutor hints in group medical PBL.

Abstract
========
OBJECTIVE: To develop a computer based learning (CBL) program in diabetes management for health care providers and academic staff
MATERIAL AND METHOD:
========
A CBL program was developed using "Authorware Professional ver. 6. 0" software. Content validation, computer background survey and investigation of the usability of the program, was conducted as part of the production of this program. The involved participants were university staff hospital care providers (e.g., doctors, nurses and pharmacists) and nursing & pharmacy students.
RESULTS:
========
Overall, the results were positive. Some limitations regarding computer background were revealed Few of the participants were familiar with self-learning materials. The usability of the CBL program was generally encouraging however some comments were made regarding program function, such as the duration of the program, and minor problems with the audiovisual effects. All comments were noted and addressed for future implementation.
CONCLUSION:
========
The CBL program was found to be a user-friendly, interactive multimedia program for diabetes management.

Abstract
========
Sketching is ubiquitous in medicine. Physicians commonly use sketches as part of their note taking in patient records and to help convey diagnoses and treatments to patients. Medical students frequently use sketches to help them think through clinical problems in individual and group problem solving. Applications ranging from automated patient records to medical education software could benefit greatly from the richer and more natural interfaces that would be enabled by the ability to understand sketches. In this paper we take the first steps toward developing a system that can understand anatomical sketches. Understanding an anatomical sketch requires the ability to recognize what anatomical structure has been sketched and from what view (e.g. parietal view of the brain), as well as to identify the anatomical parts and their locations in the sketch (e.g. parts of the brain), even if they have not been explicitly drawn. We present novel algorithms for sketch recognition and for part identification. We evaluate the accuracy of the recognition algorithm on sketches obtained from medical students. We evaluate the part identification algorithm by comparing its results to the judgment of an experienced physician.

Abstract
========
This paper describes COMET, a collaborative intelligent tutoring system for medical problem based learning. COMET uses Bayesian networks to model individual student knowledge and activity, as well as that of the group. Generic domain independent tutoring algorithms use the models to generate tutoring hints. We present an overview of the system and then the results of two evaluation studies. The validity of the modeling approach is evaluated in the areas of head injury, stroke and heart attack. Receiver operating characteristic (ROC) curve analysis indicates that, the models are accurate in predicting individual student actions. Comparison of learning outcomes shows that student clinical reasoning gains from our system are significantly higher than those obtained from human tutored sessions (Mann-Whitney, p = 0.011).

Abstract
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= Since problem solving in group problem-based learning is a collaborative process, modeling individuals and the group is necessary if we wish to develop an intelligent tutoring system that can do things like focus the group discussion, promote collaboration, or suggest peer helpers. We have used Bayesian networks to model individual student knowledge and activity, as well as that of the group. The validity of the approach has been tested with student models in the areas of head injury, stroke and heart attack. Receiver operating characteristic (ROC) curve analysis shows that, the models are highly accurate in predicting individual student actions. Comparison with human tutors shows that group activity determined by the model agrees with that suggested by the majority of the human tutors with a high degree of statistical agreement (McNemar test, p = 0.774, Kappa = 0.823)

Abstract
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In this paper, we describe the operation of barter trade exchanges by identifying key techniques used by trade brokers to stimulate trade and satisfy member needs, and present algorithms to automate some of these techniques. In particular, we develop algorithms that emulate the practice of trade brokers by matching buyers and sellers in such a way that trade volume is maximized while the balance of trade is maintained as much as possible. We model the trade balance problem as a minimum cost circulation problem (MCC) on a network. When the products have uniform cost or when the products can be traded in fractional units, we solve the problem exactly. Otherwise, we present a novel stochastic rounding algorithm that takes the fractional optimal solution to the trade balance problem and produces a valid integer solution. We then make use of a greedy heuristic that attempts to match buyers and sellers so that the average number of suppliers that a buyer must use to satisfy a given product need is minimized. We present results of empirical evaluation of our algorithms on test problems and on simulations built using data from an operating trade exchange.

ABSTRACT
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In this paper, we describe the operation of barter trade exchanges by identifying key techniques used by trade brokers to stimulate trade and satisfy member needs, and present algorithms to automate some of these techniques. In particular, we develop algorithms that emulate the practice of trade brokers by matching buyers and sellers in such a way that trade volume is maximized while the balance of trade is maintained as much as possible. We show that the buyer/seller matching and trade balance problems can be decoupled, permitting efficient solution as well as numerous options for matching strategies. We model the trade balance problem as a minimum cost circulation problem (MCC) on a network. When the products have uniform cost or when the products can be traded in fractional units, we solve the problem exactly. Otherwise, we present a novel stochastic rounding algorithm that takes the fractional optimal solution to the trade balance problem and produces a valid integer solution. We then make use of a greedy heuristic that attempts to match buyers and sellers so that the average number of suppliers that a buyer must use to satisfy a given product need is minimized. We present results on the empirical evaluation of our algorithms on test problems and simulations. Experiments show that our algorithm (MCC + stochastic rounding) runs in a fraction of the time of a commercial mixed integer programming (MIP) package while producing solutions that are always within 0.7% of the MIP solution. We evaluate the effectiveness of our algorithm on maintaining balance and on stimulating trade using two different simulation techniques, both based on transaction history data from a trade exchange. The simulation results support the barter trade exchange rule of thumb that maximizing single-period trade volume while maintaining balance of trade helps to maximize trade volume over the long run.

Abstract
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We present TRADES, a data-driven agent-based simulator for barter trade exchanges. We provide an overview of the barter trade exchange industry, focusing on the operational aspects of trade exchanges and motivating the design of our simulator. Our simulator is built by learning probabilistic models of company purchase behavior using transaction history data from an operating trade exchange. We quantitatively evaluate the accuracy of our simulator by comparing simulated trade to the transaction data, showing a high degree of agreement between the two. We also demonstrate use of the simulator to evaluate the effectiveness of a particular trade brokering strategy.

Abstract
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Preparing multi-media rich e-learning content is a labour-intensive process, requiring great time investment from content experts and multi-media designers. This high labour cost is particularly acute in fields in which knowledge changes rapidly. Keeping material current requires periodic review and updating of the material. Thus there is a need for tools to facilitate the updating of asynchronous e-learning material. While much software exists for content creation, content maintenance has received relatively little attention. We present an Learning Content Management System (LCMS) that addresses two aspects of this problem: updating course structure and updating course content. Our approach organizes course material into modular units and externally specifies course structure. We introduce the notion of Updatable Content Unit (UCU). The content author can define such units at content creation time or any time there after. Our system provides functions to search for UCUs, edit them, and integrate them back into the surrounding material.

Abstract
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= In this paper we propose to model a negotiator's decision-making behavior, expressed as preferences between an offer/counter-offer gamble and a certain offer, by learning from implicit choices that can be inferred from observed negotiation actions. The agent's actions in a negotiation sequence provide information about his preferences and risk-taking behavior. We show how offers and counter-offers in negotiation can be transformed into gamble questions providing a basis for inferring implicit preferences. Finally, we present the results of experiments and evaluation we have undertaken.

ABSTRACT
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This paper describes COMET, a collaborative intelligent tutoring system for medical problem-based learning. The system uses Bayesian networks to model individual student knowledge and activity, as well as that of the group. It incorporates a multi-modal interface that integrates text and graphics so as to provide a rich communication channel between the students and the system, as well as among students in the group. Students can sketch directly on medical images, search for medical concepts, and sketch hypotheses on a shared workspace. The prototype system incorporates substantial domain knowledge in the area of head injury diagnosis. A major challenge in building COMET has been to develop algorithms for generating tutoring hints. Tutoring in PBL is particularly challenging since the tutor should provide as little guidance as possible while at the same time not allowing the students to get lost. From studies of PBL sessions at a local medical school, we have identified and implemented eight commonly used hinting strategies. We compared the tutoring hints generated by COMET with those of experienced human tutors. Our results show that COMET's hints agree with the hints of the majority of the human tutors with a high degree of statistical agreement (McNemar test, p = 0.652, Kappa = 0.773).

Work on student modeling in automated tutoring has typically focused on very structured domains such as math and physics, where problem solving amounts to applying one of a set of formulas or techniques to work toward a solution. In this paper, we address the problem of student modeling in medical problem-based learning. This domain is challenging due to the complexity of the knowledge involved and the lack of standard, commonly accepted problem-solving techniques. This means that we must first attempt to identify prototypical patterns of reasoning and then formalize them to create a student model.
We present a Bayesian network student clinical reasoning model for automated tutoring in medical problem-based learning. The probabilistic model integrates: (1) the hypothesis structure based on the differential diagnosis of the medical case; and (2) the application of medical concepts in the problem solving process. A hierarchical scale-based system for representing medical concepts including human anatomy and patho-physiology is used to facilitate student memorization of the domains and to ease information access. The paper focuses on describing how we defined the structure and parameters of the model. We start from an initial model built based on information extracted from medical textbooks and from interviews with experts. The initial model is refined by learning structure and parameters using data collected from a medical problem-based learning tutorial. We describe a prototype tutoring system in the area of head injury diagnosis. The system communicates with the student in terms of simple dialog and medical images. We present an algorithm for determining the most likely current reasoning path of a student based on student utterances and show how to generate tutorial hints in the form of utterances and presented images.

Abstract
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We study the problem of defining similarity measures on preferences from a decision-theoretic point of view. We propose a similarity measure, called probabilistic distance, that originates from the Kendall's tau function, a well-known concept in the statistical literature. We compare this measure to other existing similarity measures on preferences. The key advantage of this measure is its extensibility to accommodate partial preferences and uncertainty. We develop efficient methods to compute this measure, exactly or approximately, under all circumstances. These methods make use of recent advances in the area of Markov chain Monte Carlo simulation. We discuss two applications of the probabilistic distance: in the construction of the Decision-Theoretic Video Advisor (diva), and in robustness analysis of a theory refinement technique for preference elicitation.

Abstract
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We present an approach to elicitation of user preference models in which assumptions can be used to guide but not constrain the elicitation process. We demonstrate that when domain knowledge is available, even in the form of weak and somewhat inaccurate assumptions, significantly less data is required to build an accurate model of user preferences than when no domain knowledge is provided. This approach is based on the KBANN (Knowledge-Based Artificial Neural Network) algorithm pioneered by Shavlik and Towell (1989). We demonstrate this approach through two examples, one involves preferences under certainty, and the other involves preferences under uncertainty. In the case of certainty, we show how to encode assumptions concerning preferential independence and monotonicity in a KBANN network, which can be trained using a variety of preferential information including simple binary classification. In the case of uncertainty, we show how to construct a KBANN network that encodes certain types of dominance relations and attitude toward risk. The resulting network can be trained using answers to standard gamble questions and can be used as an approximate representation of a person's preferences. We empirically evaluate our claims by comparing the KBANN networks with simple backpropagation artificial neural networks in terms of learning rate and accuracy. For the case of uncertainty, the answers to standard gamble questions used in the experiment are taken from an actual medical data set first used by Miyamoto and Eraker (1988). In the case of certainty, we define a measure to which a set of preferences violate a domain theory, and examine the robustness of the KBANN network as this measure of domain theory violation varies.

Abstract
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Interest in such diverse problems as development of user-adaptive software and greater involvement of patients in medical treatment decisions has increased interest in development of automated preference elicitation tools. A design challenge of these tools is to elicit reliable information while not overly fatiguing the interviewee. We address this problem by using domain background knowledge in a flexible manner. In particular, we use knowledge-based artificial neural networks to encode assumptions about a decision maker's preferences. The network is then trained using answers to standard gamble type questions. We explore the use of a domain theory encoding simple monotonicity assumptions and another additionally encoding assumptions concerning attitude toward risk. We present empirical results using a data set of real patient preferences showing that learning speed and accuracy increase as more domain knowledge is included in the neural net.

Abstract
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In previous work cite{Ha98:Towards} we presented a case-based approach to eliciting and reasoning with preferences. A key issue in this approach is the definition of similarity between user preferences. We introduced the probabilistic distance as a measure of similarity on user preferences, and provided an algorithm to compute the distance between two partially specified {em value} functions. This is for the case of decision making under {em certainty}. In this paper we address the more challenging issue of computing the probabilistic distance in the case of decision making under{em uncertainty}. We provide an algorithm to compute the probabilistic distance between two partially specified {em utility} functions. We demonstrate the use of this algorithm with a medical data set of partially specified patient preferences,where none of the other existing distancemeasures appear definable. Using this data set, we also demonstrate that the case-based approach to preference elicitation isapplicable in domains with uncertainty. Finally, we provide a comprehensive analytical comparison of the probabilistic distance with some existing distance measures on preferences.

Abstract
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We present an approach to elicitation of user preference models in which assumptions can be used to guide but not constrain the elicitation process. We show how to encode assumptions concerning preferential independence and monotonicity in a Knowledge-Based Artificial Neural Network. We quantify the degree to which user preferences violate a set of assumptions. We empirically compare the KBANN network with an unbiased ANN in terms of learning rate and accuracy for preferences consistent and inconsistent with the assumptions. We go on to demonstrate how the technique can be used to learn a fine-grained preference structure from simple binary classification data.

Abstract
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Interest in such diverse problems as development of useradaptive software and greater involvement of patients in medical treatment decisions has increased interest in development of automated preference elicitation tools. A design challenge of these tools is to elicit reliable information while not overly fatiguing the interviewee. We address this problem by using domain background knowledge in a flexible manner. In particular, we use knowledge-based artificial neural networks to encode assumptions about a decision maker's preferences. The network is then trained using answers to standard gamble type questions. We explore the use of a domain theory encoding simple monotonicity assumptions and another additionally encoding assumptions concerning attitude toward risk. We present empirical results using a data set of real patient preferences showing that learning speed and accuracy increase as more domain knowledge is included in the neural net.

The task of preference elicitation is characterized by the conflicting goals of eliciting a model as accurately as possible and asking as few questions of the user as possible. To reduce the complexity of elicitation, traditional approaches involve making independence assumptions and then performing elicitation within the constraints of those assumptions. Inaccurate assumptions can result in inaccurate elicitation. Ideally, we would like to make assumptions that approximately apply to a large segment of the population and correct for inaccuracies in those assumptions when we encounter an individual to whom they do not apply. We present an approach to encoding of soft assumptions using the KBANN technique. We show how to encode assumptions concerning preferential independence and monotonicity. We explore two different encodings of such assumptions and empirically compare them and an unbiased ANN in terms of learning rate and accuracy for additive value functions.

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Abstract
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This paper describes DIVA, a decision-theoretic agent for recommending movies that contains a number of novel features. DIVA represents user preferences using pairwise comparisons among items, rather than numeric ratings. It uses a novel similarity measure based on the concept of the probability of conflict between two orderings of ? items. The system has a rich representation of preference, distinguishing between a user's general taste in movies and his immediate interests. It takes an incremental approach to preference elicitation in which the user can provide feedback if not satisfied with the recommendation list. We empirically evaluate the performance of the system using the EachMovie collaborative filtering database

Abstract
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Bayesian networks are a powerful method for building probability models. But the formalism does not support incremental model development and reuse of models. This is partly due to the fact that Bayesian networks require precise probability values, while incremental model development and model reuse require the ability to abstract probability information. We present a formalism called hybrid Bayesian networks that combines the traditional formalism with that of qualitative probabilistic networks [5]. Qualitative probabilistic networks represent probability information with signs showing directionality of influence between random variables. Our formalism allows a model builder to start by specifying only qualitative influences and then add quantitative information as it is available and as time permits. The modeling environment can infer bounds on unspecified probability values based on those specified and on the type of qualitative influence

Abstraction
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Most medical decision problems are exceedingly complex and contain a large number of variables. Abstraction facilitates the process of building a decision model by allowing a model builder to work at a level of detail that he is most comfortable with; it is also useful in time-critical situations or when there is insuf- ficient data to support complete specification of probabilities of the uncertain events. In this paper, we identify and formalize abstraction and refinement operations commonly used in model construction. We illustrate the use of these mechanisms with an example on the follow-up management of colorectal cancer patients after surgery.

Abstract
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The need to help people choose among large numbers of items and to fiter through large amounts of information has led to a flood of research in constrution of personal' recommendation agents. One of the central issues in constructing such agents is the representation and elicitation of user preferences or interests. This topic has long been studied in Decision Theory, but surprisingly little work in the area of recommender systems has made use of formal decision-theoretic techniques. This paper describes DIVA, a decision-theoretic agent for recommending movies that contains a number of novel features. DIVA represents user preferences using pairwise comparisons among items, rather than numeric ratings. It uses a novel similarity measure based on the concept of the probability of conflict between two orderings of items. The system has a rich representation of preference, distinguishing between a user's general taste in movies and his immediate interests. It takes an incremental approach to preference elicitation in which the user can provide feedback if not satisfid with the recommendation Jist. We empirically evaluate the performance of the system using the EachMovie collaborative filtering database.

Abstract
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Classical Decision Theory provides a normative framework for representing and reasoning about complex preferences. Straight forward application of this theory to automate decision making is difficult due to high elicitation cost. In response to this problem, researchers have recently developed a number of qualitative, logic-oriented approaches for representing and reasoning about preferences. While effectively addressing some expressiveness issues, these logics have not proven powerful enough for building practical automated decision making systems. In this paper we present a hybrid approach to preference elicitation and decision making that is grounded in classical multi-attribute utility theory, but can make effective use of the expressive power of qualitative approaches. Specifically, assuming a partially specified multilinear utility function, we show how comparative statements about classes of decision alternatives can be used to further constrain the utility function and thus identify supoptimal alternatives. This work demonstrates that quantitative and qualitative approaches can be synergistically integrated to provide effective and flexible decision support.

Abstract
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The last five years have seen a surge in interest in the use of techniques from Bayesian decision theory to address problems in AI. Decision theory provides a normative framework for representing and reasoning about decision problems under uncertainty. Within the context of this framework, researchers in uncertainty in the AI community have been developing computational techniques for building rational agents and representations suited to engineering their knowledge bases. This special issue reviews recent research in Bayesian problem-solving techniques. The articles cover the topics of inference in Bayesian networks, decision-theoretic planning, and qualitative decision theory. Here, I provide a brief introduction to Bayesian networks and then cover applications of Bayesian problem-solving techniques, knowledge-based model construction and structured representations, and the learning of graphical probability models.

Abstract
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This paper presents an application of Bayesian networks (BN) to the problem of reliability assessment of power systems. Bayesian networks provide a flexible means of representing and reasoning with probabilistic information. Uncertainty and dependencies are easily incorporated in the analysis. Efficient probabilistic inference algorithms in Bayesian networks permit not only computation of the loss of load probability but also answering various probabilistic queries about the system. The advantages of BN models for power system reliability evaluation are demonstrated through examples. Results of a reliability case study of a multi-area test system are also reported

We describe ongoing work toward development of a decision-theoretic agent to help users choose videos based on their preferences. The DIVA (DecisionTheoretic Interactive Video Advisor) system elicits user preferences using a case-based technique. Hard constraints are used to permit the user to communicate temporary deviations from his basic preferences. If the user is not happy with the system's recommendations, he can provide feedback, which is used to modify the represented preferences and generate a new set of recommendations. We describe the fundamental algorithms, the implementation, and some results from some initial experimentation. Introduction We are interested in exploring the issues involved in providing users with preference-based access to information. Preference-based search is required, for example, when a user wishes to search an online catalog for a desired item or items but is unfamiliar with the domain or with the exact contents of the catalog.

Abstract
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While decision theory provides an appealing normative framework for representing rich preference structures, eliciting utility or value functions typically incurs a large cost. For many applications involving interactive systems this overhead precludes the use of formal decision-theoretic models of preference. Instead of performing elicitation in a vacuum, it would be useful if we could augment directly elicited preferences with some appropriate default information. In this paper we propose a case-based approach to alleviating the preference elicitation bottleneck. Assuming the existence of a population of users from whom we have elicited complete or incomplete preference models, we propose eliciting the preference model of a new user interactively and incrementally, using the closest existing preference models as potential defaults. A notion of closeness demands a measure of distance among preference orders, which is formally defined and investigated.

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The need to reason with imprecise probabilities arises in a wealth of situations ranging from pooling of knowledge from multiple experts to abstraction-based probabilistic planning. Researchers have typically represented imprecise probabilities using intervals and have developed a wide array of different techniques to suit their particular requirements. In this paper we provide an analysis of some of the central issues in representing and reasoning with interval probabilities. We use the well developed area of convex geometry as the underlying foundation for our analysis. In particular, we point out the ubiquity of the probability cross-pro duct operator, a generalization of which is known in the convex geometry literature as the Minkowski operator. We perform an extensive study of this operator, the result of which provides insight in to the sources of the strengths and weaknesses of various approaches to handling probability intervals. We demonstrate the application of our results to the problems of inference in interval Bayesian net works and projection of abstract probabilistic actions.

Abstract
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This paper argues for a modal view of probability. The syntax and semantics of one particularly strong probability logic are discussed and some examples of the use of the logic are provided. We show that it is both natural and useful to think of probability as a modal operator. Contrary to popular belief in AI, a probability ranging between 0 and 1 represents a continuum between impossibility and necessity, not between simple falsity and truth. The present work provides a clear semantics for quantification into the scope of the probability operator and for higher-order probabilities. Probability logic is a language for expressing both probabilistic and logical concepts.

Abstract
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While decision theory provides an appealing normative framework for representing rich preference structures, eliciting utility or value functions typically incurs a large cost. For many applications involving interactive systems this overhead precludes the use of formal decision-theoretic models of preference. Instead of performing elicitation in a vacuum, it would be useful if we could augment directly elicited preferences with some appropriate default information. In this paper we propose a case-based approach to alleviating the preference elicitation bottleneck. Assuming the existence of a population of users from whom we have elicited complete or incomplete preference structures, we propose eliciting the preferences of a new user interactively and incrementally, using the closest existing preference structures as potential defaults. Since a notion of closeness demands a measure of distance among preference structures, this paper takes the first step of studying various distance measures over fully and partially specified preference structures. We explore the use of Euclidean distance, Spearman's footrule, and define a new measure, the probabilistic distance. We provide computational techniques for all three measures.

Abstract
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A realistic system for planning with uncertain information in partially observable domains must be able to reason about sensing actions and to condition its further actions on the sensed information. Among implemented planning systems, we can distinguish two approaches to contingent decision-theoretic planning. The first is characterized by a highly unconstrained plan space, while the second is characterized by a constrained and inflexible specification of plan space. In this paper, we take a middle ground between these two approaches that we consider to be more practical. We permit the user to specify the structure of the space of possible plans to be considered but to do so in a flexible manner. This flexibility is obtained through the use of a modular representation. We separate the representation of actions from the representation of domain relations and we separate those from the representation of the plan space. Actions and domain relations are represented with schematic Bayes net fragments and plan space is represented using programming language constructs. We present a planning system that can find optimal plans given this representation.

Abstract
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The need to reason with imprecise probabilities arises in a wealth of situations ranging from pooling of knowledge from multiple experts to abstraction-based probabilistic planning. Researchers have typically represented imprecise probabilities using intervals and have developed a wide array of different techniques to suit their particular requirements. In this paper we provide an analysis of some of the central issues in representing and reasoning with interval probabilities. At the focus of our analysis is the probability cross-product operator and its interval generalization, the cc-operator. We perform an extensive study of these operators relative to manipulation of sets of probability distributions. This study provides insight into the sources of the strengths and weaknesses of various approaches to handling probability intervals. We demonstrate the application of our results to the problems of inference in interval Bayesian networks and projection and evaluation of abstract probabilistic plans.

Abstract
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AI planning agents are goal-directed: success is measured in terms of whether an input goal is satisfied. The goal gives structure to the planning problem, and planning representations and algorithms have been designed to exploit that structure. Strict goal satisfaction may be an unacceptably restrictive measure of good behavior, however. A general decision-theoretic agent, on the other hand, has no explicit goals: success is measured in terms of an arbitrary preference model or utility function defined over plan outcomes. Although it is a very general and powerful model of problem solving, decision-theoretic choice lacks structure, which can make it difficult to develop effective plan-generation algorithms. This paper establishes a middle ground between the two models. We extend the traditional AI goal model in several directions: allowing goals with temporal extent, expressing preferences over partial satisfaction of goals, and balancing goal satisfaction against the cost of the resources consumed in service of the goals. In doing so we provide a utility model for a goal-directed agent. An important quality of the proposed model is its tractability. We claim that our model, like classical goal models, makes problem structure explicit. This structure can then be exploited by a problem-solving algorithm. We support this claim by reporting on two implemented planning systems that adopt and exploit our model.

Abstract
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This is a preliminary report on research towards the development of an abstraction-based framework for decision-theoretic planning using Bayesian networks. We discuss two problems: the representation of sets of probability distributions for Bayesian networks, and the issue of representing and abstracting actions using Bayesian networks. For the first problem, we propose the use of cc-trees to represent sets of probability distributions and show how propagation in Bayesian networks can be performed without loss of information in this representation. The cc-tree representation provides an intuitive and flexible way to make tradeoffs between precision and computational cost. For the second problem, we identify a class of planning problems where a simple abstraction technique can be used to abstract a set of actions and to reduce the cost of plan evaluation.

Abstract
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Decision theory has become widely accepted in the AI community as a useful framework for planning and decision making. Applying the framework typically requires elicitation of some form of probability and utility information. While much work in AI has focused on providing representations and tools for elicitation of probabilities, relatively little work has addressed the elicitation of utility models. This imbalance is not particularly justified considering that probability models are relatively stable across problem instances, while utility models may be different for each instance. Spending large amounts of time on elicitation can be undesirable for interactive systems used in low-stakes decision making and in time-critical decision making. In this paper we investigate the issues of reasoning with incomplete utility models. We identify patterns of problem instances where plans can be proved to be suboptimal if the (unknown) utility function satisfies certain conditions. We present an approach to planning and decision making that performs the utility elicitation incrementally and in a way that is informed by the domain model.

Abstract
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We define a language for representing context-sensitive probabilistic knowledge. A knowledge base consists of a set of universally quantified probability sentences that include context constraints, which allow inference to be focused on only the relevant portions of the probabilistic knowledge. We provide a declarative semantics for our language. We present a query answering procedure which takes a query Q and a set of evidence E and constructs a Bayesian network to compute P (QjE). The posterior probability is then computed using any of a number of Bayesian network inference algorithms. We use the declarative semantics to prove the query procedure sound and complete. We use concepts from logic programming to justify our approach.

Abstract
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Bayesian networks use the techniques of probability theory to reason under uncertainty, and have become an important formalism for medical decision support systems.We describe the development and validation of a Bayesian network (MammoNet) to assist in mammographic diagnosis of breast cancer. MammoNet integrates five patient-history features, two physical findings, and 15 mammographic features extracted by experienced radiologists to determine the probability of malignancy. We outline the methods and issues in the system's design, implementation, and evaluation. Bayesian networks provide a potentially useful tool for mammographic decision support.

Abstract
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We present an educational tool for bringing the information contained in a Bayesian network to the end user in an easily intelligible form. The BANTER shell is designed to tutor users in evaluation of hypotheses and selection of optimal diagnostic procedures. BANTER can be used with any Bayesian network containing nodes that can be classified into hypotheses, observations, and diagnostic procedures. The system enables one to present various types of queries to the network, to test one's ability to select optimal diagnostic procedures, and the request explanations. We describe the system's capabilities by illustrating how it functions with two structurally different network models of real-world medical problems.

Abstract
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We present a language for representing context-sensitive temporal probabilistic knowledge. Context constraints allow inference to be focused on only the relevant portions of the probabilistic knowledge. We provide a declarative semantics for our language. We present a sound and complete algorithm for computing posterior probabilities of temporal queries, as well as an efficient implementation of the algorithm. Throughout we illustrate the approach with the problem of reasoning about the effects of medications and interventions on the state of a patient in cardiac arrest. We empirically evaluate the efficiency of our system by comparing its inference times on problems in this domain with those of standard Bayesian network representations of the problems.

Abstract
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We present a framework for representing the probabilistic effects of actions and contingent treatment plans. Our language has a well-defined declarative semantics and we have developed an implemented algorithm (named BNG) that generates Bayesian networks (BN) to compute the posterior probabilities of queries. In this paper we address the problem of projecting a contingent treatment plan by automatically constructing a structure of interrelated BNs, which we call a BN-graph, and applying the available propagation procedures on it. To address the optimal plan generation, we base our approach on the observation that normally the target plan space has a well-defined structure. We provide a language to describe plan spaces which resembles a programming language with loops and conditionals. We briefly present the procedures for finding the optimal plan(s) from such specified plan spaces.

Abstract
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This paper provides a formal and practical framework for sound abstraction of probabilistic actions. We start by precisely defining the concept of sound abstraction within the context of finite-horizon planning (where each plan is a finite sequence of actions). Next we show that such abstraction cannot be performed within the traditional probabilistic action representation, which models a world with a single probability distribution over the state space. We then present the constraint mass assignment representation, which models the world with a set of probability distributions and is a generalization of mass assignment representations. Within this framework, we present sound abstraction procedures for three types of action abstraction. We end the paper with discussions and related work on sound and approximate abstraction. We give pointers to papers in which we discuss other sound abstraction-related issues, including applications, estimating loss due to abstraction, and automatically generating abstraction hierarchies .

Abstract
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Modelling worlds and actions under uncertainty is one of the central problems in the framework of decision-theoretic planning. The representation must be general enough to capture real-world problems but at the same time it must provide a basis upon which theoretical results can be derived. The central notion in the framework we propose here is that of the affine-operator, which serves as a tool for constructing (convex) sets of probability distributions, and which can be considered as a generalization of belief functions and interval mass assignments. Uncertainty in the state of the worlds is modeled with sets of probability distributions, represented by affine-trees, while actions are defined as tree-manipulators. A small set of key properties of the affine-operator is presented, forming the basis for most existing operator-based definitions of probabilistic action projection and action abstraction. We derive and prove correct three projection rules, which vividly illustrate the precision-complexity tradeoff in plan projection. Finally, we show how the three types of action abstraction identified by Haddawy and Doan are manifested in the present framework.

Abstract
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Decision-theoretic refinement planning is a new technique for finding optimal courses of action. The authors sought to determine whether this technique could identify optimal strategies for medical diagnosis and therapy. An existing model of acute deep venous thrombosis of the lower extremities was encoded for analysis by the decision-theoretic refinement planning system (DRIPS). The encoding represented 6,206 possible plans. The DRIPS planner used artificial intelligence techniques to eliminate 5,150 plans (83%) from consideration without examining them explicitly. The DRIPS system identified the five strategies that minimized cost and mortality. The authors conclude that decision-theoretic planning is useful for examining large medical-decision problems.

Abstract
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Any anytime algorithm used for decision-making should h ave the property that it considers the most important aspects of the decision problem first. In this way, the algorithm can first eliminate disastrous decisions and recognize particularly advantageous decisions, considering finer detail& if time permits. We view planning as a decision-making process and discuss th t: design of anytime algorithms for decision theoretic planning. In particular, we present an anytime decision-theoretic planner that uses abstraction to focus attention first on those aspects of a planning problem that have the highest impact on expected utility. We discuss control schemes for refining this behavior and methods for automatically creating good abstractions. We present an intelligent agent architecture for integrating our anytime planner with an execution module and describe the status of the implementation

Abstract
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We present a first-order logic of time, chance, and probability that is capable of expressing the relation between subjective probability and objective chance at different times. Using this capability, we show how the logic can distinguish between causal and evidential correlation by distinguishing between conditions, events, and actions that 1) influence the agent's belief in chance and 2) the agent believes to influence chance. Furthermore, the semantics of the logic captures common sense inferences concerning objective chance and causality. We show that an agent's subjective probability is the expected value of its beliefs concerning objective chance. We also prove that an agent using this representation believes with certainty that the past cannot be causally influence.

Abstract
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This paper integrates logical and probabilistic approaches to the representation of planning problems by developing a first-order logic of time, chance, and action. We start by making explicit and precise common sense notions about time, chance, and action central to the planning problem. We then develop a logic, the semantics of which incorporates these intuitive properties. The logical language integrates both modal and probabilistic constructs and allows quantification over time points, probability values, and domain individuals. Probability is treated as a sentential operator in the language, so it can be arbitrarily nested and combined with other logical operators. The language can represent the chance that facts hold and events occur at various times. It can represent the chance that actions and other events affect the future. The model of action distinguishes between action feasibility, executability, and effects. We present a proof theory for the logic and show how the logic can be used to describe actions in such a way that the action descriptions can be composed to infer properties of plans via the proof theory.

Abstract
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Any anytime algorithm used for decision-making should h ave the property that it considers the most important aspects of tht: decision problem first. In this way, the algorithm ca.n first eliminate disastrous decisions and recognize particularly advantageous decisions, considering finer detail& if time permits. We view planning as a decision-making; process and discuss the design of anytime algorithms for decision theoretic planning. In particular, we present an anytime decision-theoretic planner that uses abstraction to focus attention first on those aspects of a planning problem that have the highest impact on expected utility. We discuss control schemes for refining this behavior and methods for automatically creating good abstractions. We present an intelligent agent architecture for integrating our anytime planner with an execution module and describe the status of the implementation.

ABSTRACT
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We describe the early stages of the development and validation of a Bayesian network to assist in the detection of breast cancer. MammoNet integrates mammographic findings, demographic factors, and physical examination to determine the probability of malignancy. Conditional probabilities were obtained from the medical literature and from expert opinion. Problems (and solutions) encountered while developing the model are discussed. MammoNet is implemented as a knowledge base of rules; problem-specific networks are constructed using a Bayesian network construction algorithm. The model's performance was evaluated with 77 cases drawn from a textbook and a clinical teaching file; MammoNet performed well, and achieved an area under the receiver operating curve of 0.881 (? 0.045).

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Abstract
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We present a probabilistic logic programming framework that allows the representation of conditional probabilities. While conditional probabilities are the most commonly used method for representing uncertainty in probabilistic expert systems, they have been largely neglected by work in quantitative logic programming. We define a fixpoint theory, declarative semantics, and proof procedure for the new class of probabilistic logic programs. Compared to other approaches to quantitative logic programming, we provide a true probabilistic framework with potential applications in probabilistic expert systems and decision support systems. We also discuss the relationship between such programs and Bayesian networks, thus moving toward a unification of two major approaches to automated reasoning.

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Clinical decision analysis seeks to identify the optimal management strategy by modelling the uncertainty and risks entailed in the diagnosis, natural history, and treatment of a particular problem or disorder. Decision trees are the most frequently used model in clinical decision analysis, but can be tedious to construct, cumbersome to use, and computationally prohibitive, especially with large, complex decision problems. We present a new method for clinical decision analysis that combines the techniques of decision theory and artificial intelligence. Our model uses a modular representation of knowledge that simplifies model building and enables more fully automated decision making. Moreover, the model exploits problem structures to yield better computational efficiency. As an example we apply our techniques to the problem of management of acute deep venous thrombosis.

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We present a language for representing context-sensitive temporal probabilistic knowledge. Context constraints allow inference to be focused on only the relevant portions of the probabilistic knowledge. We provide a declarative semantics for our language and an implemented algorithm (BNG) that generates Bayesian networks to compute the posterior probabilities of queries. We illustrate the use of the BNG system by applying it to the problem of modeling the effects of medications and other interventions on the condition of a patient in cardiac arrest.

Abstract
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Bayesian networks use the techniques of probability theory to reason under conditions of uncertainty. We investigated the use of Bayesian networks for radiological decision support. A Bayesian network for the interpretation of mammograms (MammoNet) was developed based on five patient-history features, two physical findings, and 15 mammographic features extracted by experienced radiologists. Conditional-probability data, such as sensitivity and specificity, were derived from peer-reviewed journal articles and from expert opinion. In testing with a set of 77 cases from a mammography atlas and a clinical teaching file, MammoNet performed well in distinguishing between benign and malignant lesions, and yielded a value of 0.881 (+/- 0.045) for the area under the receiver operating characteristic curve. We conclude that Bayesian networks provide a potentially useful tool for mammographic decision support.

Abstract
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We define a context-sensitive temporal probability logic for representing classes of discrete-time temporal Bayesian networks. Context constraints allow inference to be focused on only the relevant portions of the probabilistic knowledge. We provide a declarative semantics for our language. We present a Bayesian network construction algorithm whose generated networks give sound and complete answers to queries. We use related concepts in logic programming to justify our approach. We have implemented a Bayesian network construction algorithm for a subset of the theory and demonstrate it's application to the problem of evaluating the effectiveness of treatments for acute cardiac conditions.

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This paper discusses techniques for performing efficient decision-theoretic planning. We give an overview of the DRIPS decision-theoretic refinement planning system, which uses abstraction to efficiently identify optimal plans. We present techniques for automatically generating search control information, which can significantly improve the planner's performance. We evaluate the efficiency of DRIPS both with and without the search control rules on a complex medical planning problem and compare its performance to that of a branch-and-bound decision tree algorithm.

ABSTRACT
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Objective:
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Decision-theoretic planning is a new technique for selecting optimal actions. The authors sought to determine whether decision-theoretic planning could be applied to medical decision making to identify optimal strategies for diagnosis and therapy.
Methods:
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An existing model of acute deep venous thrombosis (DVT) of the lower extremities — in which 24 management strategies were compared — was converted into a set of conditional-probabilistic actions for use by the DRIPS decision-theoretic planning system. Actions were grouped into an abstraction/decomposition hierarchy. A utility function was defined in accordance with the existing DVT management model to incorporate the costs and risks of the diagnostic tests and treatments.
Results:
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From 18 primitive actions (such as “perform venography” and “treat if venography shows thigh DVT”), a total of 312 possible concrete plans were encoded within the abstraction/decomposition hierarchy. The DRIPS planning system used abstraction techniques to eliminate 136 possible plans (44%) from consideration. It determined that, given the parameters specified, the most cost-effective management strategy was “no tests, no treatment.” This result differed from the published result of “perform ultrasonography, treat if positive.” In reviewing the original article, it was determined that DRIPS had revealed an error in the manually constructed decision trees used in that manuscript. At values for the cost of death of $75,000 and greater, the optimal strategy became “impedance plethysmography (IPG), don't wait, perform venography if IPG is positive, and treat only if venography shows thigh DVT.”
Conclusion:
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Decision-theoretic planning is applicable to medical decision making, and may be an extremely useful technique for complex decisions. The use of inheritance abstraction makes the technique computationally tractable for complex planning problems, and the modular nature of the data entry may help eliminate errors that appear in manually encoded decision trees.

Abstract
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We present a first-order logic of time, chance, and probability that is capable of expressing the relation between subjective probability and objective chance at different times. Using this capability, we show how the logic can distinguish between causal and evidential correlation by distinguishing between conditions, events, and actions that 1) influence the agent's belief in chance and 2) the agent believes to influence chance. Furthermore, the semantics of the logic captures common sense inferences concerning objective chance and causality. We show that an agent's subjective probability is the expected value of its beliefs concerning objective chance. We also prove that an agent using this representation believes with certainty that the past cannot be causally influenced.

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= We present a system that generates explanations and tutorial problems from the probabilistic information contained in Bayesian belief networks. BANTER is a tool for high-level interaction with any Bayesian network whose nodes can be classified as hypotheses, observations, and diagnostic procedures. Users need no knowledge of Bayesian networks, only familiarity with the particular domain and an elementary understanding of probability. Users can query the knowledge base, identify optimal diagnostic procedures, and request explanations. We describe BANTER's algorithms and illustrate its application to an existing medical model.

Abstract
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We present an educational tool for bringing the information contained an a Bayesian network to the end user an an easily intelligible form. The BANTER shell is designed to tutor users in evaluation of hypotheses and selection of optimal diagnostic procedures. BANTER can be used with any Bayesian network containing nodes that can be classified into hypotheses, observations, and diagnostic procedures. We present algorithms for determining optimal diagnostic procedures and for explanation generation.

Abstract
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This paper discusses the problem of abstracting conditional probabilistic actions. We identify two distinct types of abstraction: intra-action abstraction and inter-action abstraction. We define what it means for the abstraction of an action to be correct and then derive two methods of intra-action abstraction and two methods of inter-action abstraction which are correct according to this criterion. We illustrate the developed techniques by applying them to actions described with the temporal action representation used in the DRIPS decision-theoretic planner and we describe how the planner uses abstraction to reduce the complexity of planning.

Abstract
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We present a method for dynamically generating Bayesian networks from knowledge bases consisting of first-order probability logic sentences. We present a subset of probability logic sufficient for representing the class of Bayesian networks with discrete-valued nodes. We impose constraints on the form of the sentences that guarantee that the knowledge base contains all the probabilistic information necessary to generate a network. We define the concept of d-separation for knowledge bases and prove that a knowledge base with independence conditions defined by d-separation is a complete specification of a probability distribution. We present a network generation algorithm that, given an inference problem in the form of a query Q and a set of evidence E, generates a network to compute P(Q|E). We prove the algorithm to be correct.

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Abstract
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Bayesian networks, a technique for reasoning under uncertainty, currently are being developed for application to medical decision making. To explore their usefulness for radiologic decision support, a Bayesian belief network was constructed in the domain of hepatobiliary disease. The network model's nodes represent diagnoses, physical findings, laboratory test results, and imaging study findings. The connections between nodes incorporate conditional probabilities, such as sensitivity and specificity, to represent probabilistic influences. Statistical data were abstracted from peer-reviewed journal articles on hepatobiliary disease, and a network was created to reflect the data. The network successfully determined the a priori probabilities of various diseases, and incorporated laboratory and imaging results to calculate the a posteriori probabilities. The most informative examination was identified, that is, the laboratory study or imaging procedure that led to the greatest diagnostic certainty. Bayesian networks represent a very promising technique for decision support in radiology: they can assist physicians in formulating diagnoses and in selecting imaging procedures.

Abstract This paper proposes and investigates an approach to deduction in probabilistic logic, using as its medium a language that generalizes the propositional version of Nilsson's probabilistic logic by incorporating conditional probabilities. Unlike many other approaches to deduction in probabilistic logic, this approach is based on inference rules and therefore can produce proofs to explain how conclusions are drawn. We show how these rules can be incorporated into an anytime deduction procedure that proceeds by computing increasingly narrow probability intervals that contain the tightest entailed probability interval. Since the procedure can be stopped at any time to yield partial information concerning the probability range of any entailed sentence, one can make a tradeoff between precision and computation time. The deduction method presented here contrasts with other methods whose ability to perform logical reasoning is either limited or requires finding all truth assignments consistent consistent with the given sentences

Abstract
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To operate intelligently in the world, an agent must reason about its actions. The consequences of an action are a function of both the state of the world and the action itself. Many aspects of the world are inherently stochastic, so a representation for reasoning about actions must be able to express chances of world states as well as indeterminacy in the effects of actions and other events. This paper presents a propositional temporal probability logic for representing and reasoning about actions. The logic can represent the probability that facts hold and events occur at various times. It can represent the probability that actions and other events affect the future. It can represent concurrent actions and conditions that hold or change during execution of an action. The model of probability relates probabilities over time. The logical language integrates both modal and probabilistic constructs and can thus represent and distinguish between possibility, probability, and truth. Several examples illustrating the use of the logic are given.

This paper discusses the semantics and proof theory of Nilsson's probabilistic logic, outlining both the benefits of its well-defined model theory and the drawbacks of its proof theory. Within Nilsson's semantic framework, we derive a set of inference rules which are provably sound. The resulting proof system, in contrast to Nilsson's approach, has the important feature of convergence - that is, the inference process proceeds by computing increasingly narrow probability intervals which converge from above and below on the smallest entailed probability interval. Thus the procedure can be stopped at any time to yield partial information concerning the smallest entailed interval.

Abstract
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A system capable of performing approximate inferences under time constraints is presented. Censored production rules are used to represent both domain and control information. These are given a probabilistic semantics and reasoning is performed using a scheme based on Dempster-Shafer theory. Examples show the naturalness of the representation and the flexibility of the system. Suggestions for further research are offered