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17
An Algorithm for Probabilistic Planning
, 1995
"... We define the probabilistic planning problem in terms of a probability distribution over initial world states, a boolean combination of propositions representing the goal, a probability threshold, and actions whose effects depend on the executiontime state of the world and on random chance. Adoptin ..."
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Cited by 258 (18 self)
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We define the probabilistic planning problem in terms of a probability distribution over initial world states, a boolean combination of propositions representing the goal, a probability threshold, and actions whose effects depend on the executiontime state of the world and on random chance. Adopting a probabilistic model complicates the definition of plan success: instead of demanding a plan that provably achieves the goal, we seek plans whose probability of success exceeds the threshold. In this paper, we present buridan, an implemented leastcommitment planner that solves problems of this form. We prove that the algorithm is both sound and complete. We then explore buridan's efficiency by contrasting four algorithms for plan evaluation, using a combination of analytic methods and empirical experiments. We also describe the interplay between generating plans and evaluating them, and discuss the role of search control in probabilistic planning. 3 We gratefully acknowledge the comment...
Bayesian Networks Without Tears
 AI MAGAZINE
, 1991
"... I give an introduction to Bayesian networks for AI researchers with a limited grounding in probability theory. Over the last few years, this method of reasoning using probabilities has become popular within the AI probability and uncertainty community. Indeed, it is probably fair to say that Bayesia ..."
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Cited by 236 (2 self)
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I give an introduction to Bayesian networks for AI researchers with a limited grounding in probability theory. Over the last few years, this method of reasoning using probabilities has become popular within the AI probability and uncertainty community. Indeed, it is probably fair to say that Bayesian networks are to a large segment of the AIuncertainty community what resolution theorem proving is to the AIlogic community. Nevertheless, despite what seems to be their obvious importance, the ideas and techniques have not spread much beyond the research community responsible for them. This is probably because the ideas and techniques are not that easy to understand. I hope to rectify this situation by making Bayesian networks more accessible to the probabilistically unsophisticated.
Dynamic Belief Networks for Discrete Monitoring
 IEEE Transactions on Systems, Man, and Cybernetics
, 1994
"... We describe the development of a monitoring system which uses sensor observation data about discrete events to construct dynamically a probabilistic model of the world. This model is a Bayesian network incorporating temporal aspects, which we call a Dynamic Belief Network; it is used to reason under ..."
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Cited by 54 (7 self)
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We describe the development of a monitoring system which uses sensor observation data about discrete events to construct dynamically a probabilistic model of the world. This model is a Bayesian network incorporating temporal aspects, which we call a Dynamic Belief Network; it is used to reason under uncertainty about both the causes and consequences of the events being monitored. The basic dynamic construction of the network is datadriven. However the model construction process combines sensor data about events with externally provided information about agents' behaviour, and knowledge already contained within the model, to control the size and complexity of the network. This means that both the network structure within a time interval, and the amount of history and detail maintained, can vary over time. We illustrate the system with the example domain of monitoring robot vehicles and people in a restricted dynamic environment using lightbeam sensor data. In addition to presenting a ...
Localized Partial Evaluation of Belief Networks
, 1995
"... Most algorithms for propagating evidence through belief networks have been exact and exhaustive: they produce an exact (pointvalued) marginal probability for every node in the network. Often, however, an application will not need information about every node in the network nor will it need exact pr ..."
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Cited by 43 (1 self)
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Most algorithms for propagating evidence through belief networks have been exact and exhaustive: they produce an exact (pointvalued) marginal probability for every node in the network. Often, however, an application will not need information about every node in the network nor will it need exact probabilities. We present the localized partial evaluation (LPE) propagation algorithm, which computes interval bounds on the marginal probability of a specified query node by examining a subset of the nodes in the entire network. Conceptually, LPE ignores parts of the network that are "too far away" from the queried node to have much impact on its value. LPE has the "anytime" property of being able to produce better solutions (tighter intervals) given more time to consider more of the network. 1 Introduction Belief networks provide a way of encoding knowledge about the probabilistic dependencies and independencies of a set of variables in some domain. Variables are encoded as nodes in the ne...
Converting a rulebased expert system into a belief network
 Medical Informatics
, 1993
"... The theory of belief networks offers a relatively new approach for dealing with uncertain information in knowledgebased (expert) systems. In contrast with the heuristic techniques for reasoning with uncertainty employed in many rulebased expert systems, the theory of belief networks is mathematica ..."
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Cited by 37 (6 self)
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The theory of belief networks offers a relatively new approach for dealing with uncertain information in knowledgebased (expert) systems. In contrast with the heuristic techniques for reasoning with uncertainty employed in many rulebased expert systems, the theory of belief networks is mathematically sound, based on techniques from probability theory. It therefore seems attractive to convert existing rulebased expert systems into belief networks. In this article, we discuss the design of a belief network reformulation of the diagnostic rulebased expert system HEPAR. For the purpose of this experiment, we have studied several typical pieces of medical knowledge represented in the HEPAR system. It turned out that, due to the differences in the type of knowledge represented and in the formalism used to represent uncertainty, much of the medical knowledge required for building the belief network concerned could not be extracted from HEPAR. As a consequence, significant additional knowledge acquisition was required. However, the objects and attributes defined in the HEPAR system, as well as the conditions in production rules mentioning these objects and attributes were useful for guiding the selection of the statistical variables for building the belief network. The mapping of objects and attributes in HEPAR to statistical variables is discussed in detail.
Theory refinement of bayesian networks with hidden variables
 In Machine Learning: Proceedingsof the International Conference
, 1998
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Critiquing: Effective Decision Support in TimeCritical Domains
, 1996
"... The TraumAID system is a tool for assisting physicians during the initial definitive management phase of patients with severe injuries. Originally, TraumAID was conceived as a rulebased expert system combined with a planner. After this architecture had been implemented and evaluated, we began to f ..."
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Cited by 13 (3 self)
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The TraumAID system is a tool for assisting physicians during the initial definitive management phase of patients with severe injuries. Originally, TraumAID was conceived as a rulebased expert system combined with a planner. After this architecture had been implemented and evaluated, we began to face the issue of how TraumAID could communicate its plans to physicians in order to influence their behavior and have a positive effect on patient outcome. It was hypothesized that a critiquing approach, in which the system is told what actions the user intends to carry out and produces a critique in response to those intentions, might be appropriate. To meet the needs of physicians engaged in managing trauma cases, critiques must be updated and made available rapidly. They must be clear and succinct, containing only relevant information while still including enough justification ...
Computerbased Decision Support in the Management of Primary Gastric nonHodgkin Lymphoma
 Methods of Information in Medicine, 37:206–219
, 1998
"... Primary nonHodgkin lymphoma of the stomach is a rare disorder for which clinical management has not yet been settled completely. Faced with the many uncertainties associated with the selection of a treatment for a patient with this disorder, it is difficult to determine the treatment that is optima ..."
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Cited by 5 (3 self)
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Primary nonHodgkin lymphoma of the stomach is a rare disorder for which clinical management has not yet been settled completely. Faced with the many uncertainties associated with the selection of a treatment for a patient with this disorder, it is difficult to determine the treatment that is optimal for the patient, as well as the prognosis to be expected. The development of a decisiontheoretic model of nonHodgkin lymphoma of the stomach is described. The model aims to assist the clinician in exploring various clinical questions, among others questions concerning prognosis and optimal treatment. Central to the model is a probabilistic network that offers an explicit representation of the uncertainties underlying the decisionmaking process. The model has been incorporated into a computerbased system, that can be used as a decisionsupport system. Preliminary evaluation results indicate that the performance of the model in its present form matches the performance of experienced clinicians.
Knowledge acquisition for decisiontheoretic expert systems
 AISB Quarterly
, 1996
"... In this paper, the construction of decisiontheoretic expert systems in collaboration with domain experts is discussed. In particular, the role of domain models in guiding the knowledgeacquisition process is reviewed, and various techniques that may help in the design of a decisiontheoretic expert ..."
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Cited by 4 (1 self)
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In this paper, the construction of decisiontheoretic expert systems in collaboration with domain experts is discussed. In particular, the role of domain models in guiding the knowledgeacquisition process is reviewed, and various techniques that may help in the design of a decisiontheoretic expert system are presented. Treatment planning in patients with a congenital heart disease is described as an example domain. The development of a decisiontheoretic expert system for this domain is taken as a running example. 1
Belief Network Inference Algorithms: a Study of Performance Based on Domain Characterisation
, 1996
"... In recent years belief networks have become a popular representation for reasoning under uncertainty and are used in a wide variety of applications. There are a number of exact and approximate inference algorithms available for performing belief updating, however in general the task is NPhard. To o ..."
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Cited by 3 (0 self)
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In recent years belief networks have become a popular representation for reasoning under uncertainty and are used in a wide variety of applications. There are a number of exact and approximate inference algorithms available for performing belief updating, however in general the task is NPhard. To overcome the problems of computational complexity that occur when modelling larger, realworld problems, researchers have developed variants of stochastic simulation approximation algorithms, and a number of other approaches involve approximating the model or limiting belief updating to nodes of interest. Typically comparisons are made of only a few algorithms, and on a particular example network. We survey the belief network algorithms and propose a system for domain characterisation as a basis for algorithm comparison. We present performance results using this framework from three sets of experiments: (1) on the Likelihood Weighting (LW) and Logic Sampling (LS) stochastic simulation algorithms; (2) on the performance of LW and Jensen's algorithms on statespace abstracted networks, (3) some comparisons of the time performance of LW, LS and the Jensen algorithm. Our results indicate that domain characterisation may be useful for predicting inference algorithm performance on a belief network for a new application domain.