Publications

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 with the given sentences.

 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.

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 real-world 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.

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 the volume of goods traded 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 goods have uniform cost or when the goods can be traded in fractional units, we solve the problem exactly using a simplified version of the minimum mean cycle canceling algorithm. 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 sellers matched to a buyer of a good is minimized.  Finally, we also present results on the empirical evaluation of our algorithms on test problems and simulations.  The solutions from our stochastic rounding algorithm are always within 0.7% of the solution obtained from a commercial mixed integer programming package. We evaluate the effectiveness of our algorithm on maintaining balance and on stimulating trade using a simulator built using transaction history data from a trade exchange. The simulation results confirm 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.

 n 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 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 ablity to select optimal diagnostic procedures, and to request explanations. We describe the system's capabil­ ities by illustrating how it functions with two structurally different network models of real­world medical problems.

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 COMETwith that chosen by human tutors.

We present a first­order logic of time, chance, and probability that is capable of expressing the four types of higher­order probability sentences relating subjective probability and objective chance at different times. We define a causal notion of objective chance and show how it can be used in conjunction with subjective probability to 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 some commonsense 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. To appear in IEEE SMC special issue on Higher­Order Probability.

We present a first­order logic of time, chance, and probability that is capable of expressing the four types of higher­order probability sentences relating subjective probability and objective chance at different times. We define a causal notion of objective chance and show how it can be used in conjunction with subjective probability to 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 some commonsense 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. To appear in IEEE SMC special issue on Higher­Order Probability.

We present a language for representing context­ sensitive temporal probabilistic knowledge. Con­ text 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 com­ pute the posterior probabilities of queries. We il­ lustrate the use of the BNG system by applying it to the problem of modeling the effects of medica­ tions and other interventions on the condition of a patient in cardiac arrest.

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).

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).

Clinical reasoning is the cognitive process by which physicians synthesize clinical case information, integrate it with their knowledge and experience, and diagnose or manage the patient’s problem.1 Problem-based learning is a method often used to characterize medical problems as a context for students to acquire knowledge and clinical-reasoning ......

This paper compares the accuracy of Decision Tree and Bayesian Network algorithms for predicting the academic performance of undergraduate and postgraduate students at two very different academic institutes: Can Tho University (CTU), a large national university in Viet Nam; and the Asian Institute of Technology (AIT), a small international postgraduate institute in Thailand that draws students from 86 different countries. Although the diversity of these two student populations is very different, the data-mining tools were able to achieve similar levels of accuracy for predicting student performance: 73/71% for {fail, fair, good, very good} and 94/93% for {fail, pass} at the CTU/AIT respectively. These predictions are most useful for identifying and assisting failing students at CTU (64% accurate), and for selecting Very Good students for scholarships at the AIT (82% accurate). In this analysis, the Decision Tree was consistently 3-12% more accurate than the Bayesian Network. The results of these case studies give insight into techniques for accurately predicting student performance, compare the accuracy of data mining algorithms, and demonstrate the maturity of open source tools.

 In this paper 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.

We describe ongoing work toward development of a decision­theoretic agent to help users choose videos based on their preferences. The DIVA (Decision Theoretic Interactive Video Advisor) system elicits user preferences using a case­based 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.