Uncertainty in Artificial Intelligence : Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence, The Catholic University of America, Washington, D.C. 1993

Uncertainty in Artificial Intelligence contains the proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence held at the Catholic University of America in Washington, DC, on July 9-11, 1993. The papers focus on methods of reasoning and decision making under uncertainty as applied to problems in artificial intelligence (AI) and cover topics ranging from knowledge acquisition and automated model construction to learning, planning, temporal reasoning, and machine vision. Comprised of 66 chapters, this book begins with a discussion on causality in Bayesian belief networks before turning to a decision theoretic account of conditional ought statements that rectifies glaring deficiencies in classical deontic logic and forms a sound basis for qualitative decision theory. Subsequent chapters explore trade-offs in constructing and evaluating temporal influence diagrams; normative engineering risk management systems; additive belief-network models; and sensitivity analysis for probability assessments in Bayesian networks. Automated model construction and learning as well as algorithms for inference and decision making are also considered. This monograph will be of interest to both students and practitioners in the fields of AI and computer science. Content: Front Matter, Page i Copyright, Page ii Preface, Page vii, David Heckerman, Abe Mamdani, Michael P. Wellman Acknowledgements, Page viii Causality in Bayesian Belief Networks, Pages 3-11, Marek J. Druzdzel, Herbert A. Simon From Conditional Oughts to Qualitative Decision Theory, Pages 12-20, Judea Pearl A Probabilistic Algorithm for Calculating Structure: Borrowing from Simulated Annealing, Pages 23-31, Russ B. Altman A Study of Scaling Issues in Bayesian Belief Networks for Ship Classification, Pages 32-39, S.A. Musman, L.W. Chang TRADEOFFS IN CONSTRUCTING AND EVALUATING TEMPORAL INFLUENCE DIAGRAMS, Pages 40-47, Gregory M. Provan End-User Construction of Influence Diagrams for Bayesian Statistics, Pages 48-54, Harold P. Lehmann, Ross D. Shachter On Considering Uncertainty and Alternatives in Low-Level Vision, Pages 55-63, Steven M. LaValle, Seth A. Hutchinson Forecasting Sleep Apnea with Dynamic Network Models, Pages 64-71, Paul Dagum, Adam Galper Normative Engineering Risk Management Systems, Pages 72-79, Peter J. Regan Diagnosis of Multiple Faults: A Sensitivity Analysis, Pages 80-87, David Heckerman, Michael Shwe Additive Belief-Network Models, Pages 91-98, Paul Dagum, Adam Galper Parameter adjustment in Bayes networks. The generalized noisy OR–gate, Pages 99-105, F.J. Díez A fuzzy relation-based extension of Reggia's relational model for diagnosis handling uncertain and incomplete information, Pages 106-113, Didier Dubois, Henri Prade Dialectic reasoning with inconsistent information, Pages 114-121, Morten Elvang-Gøransson, Paul Krause, John Fox Causal Independence for Knowledge Acquisition and Inference, Pages 122-127, David Heckerman Utility-Based Abstraction and Categorization, Pages 128-135, Eric J. Horvitz, Adrian C. Klein Sensitivity Analysis for Probability Assessments in Bayesian Networks, Pages 136-142, Kathryn Blackmond Laskey Causal Modeling, Pages 143-151, John F. Lemmer Some Complexity Considerations in the Combination of Belief Networks, Pages 152-158, Izhar Matzkevich, Bruce Abramson Deriving a Minimal I-map of a Belief Network Relative to a Target Ordering of its Nodes, Pages 159-165, Izhar Matzkevich, Bruce Abramson Probabilistic Conceptual Network: A Belief Representation Scheme for Utility-Based Categorization, Pages 166-173, Kim Leng Poh, Michael R. Fehling Reasoning about the Value of Decision-Model Refinement: Methods and Application, Pages 174-182, Kim Leng Poh, Eric J. Horvitz Mixtures of Gaussians and Minimum Relative Entropy Techniques for Modeling Continuous Uncertainties, Pages 183-190, William B. Poland, Ross D. Shachter Valuation Networks and Conditional Independence, Pages 191-199, Prakash P. Shenoy Relevant Explanations: Allowing Disjunctive Assignments, Pages 200-207, Solomon Eyal Shimony A Generalization of the Noisy-Or Model, Pages 208-215, Sampath Srinivas Using First-Order Probability Logic for the Construction of Bayesian Networks, Pages 219-226, Fahiem Bacchus Representing and Reasoning With Probabilistic Knowledge: A Bayesian Approach, Pages 227-234, Marie des Jardins Graph-Grammar Assistance for Automated Generation of Influence Diagrams, Pages 235-242, John W. Egar, Mark A. Musen Using Causal Information and Local Measures to Learn Bayesian Networks, Pages 243-250, Wai Lam, Fahiem Bacchus Minimal Assumption Distribution Propagation in Belief Networks, Pages 251-258, Ron Musick An Algorithm for the Construction of Bayesian Network Structures from Data, Pages 259-265, Moninder Singh, Marco Valtorta A Construction of Bayesian Networks from Databases Based on an MDL Principle, Pages 266-273, Joe Suzuki Knowledge-Based Decision Model Construction for Hierarchical Diagnosis: A Preliminary Report, Pages 274-281, Soe-Tsyr Yuan A Synthesis of Logical and Probabilistic Reasoning for Program Understanding and Debugging, Pages 285-291, Lisa J. Burnell, Eric J. Horvitz An Implementation of a Method for Computing the Uncertainty in Inferred Probabilities in Belief Networks, Pages 292-300, Peter Che, Richard E. Neapolitan, James Kenevan, Martha Evens Incremental Probabilistic Inference, Pages 301-308, Bruce D'Ambrosio Deliberation Scheduling for Time-Critical Sequential Decision Making, Pages 309-316, Thomas Dean, Leslie Pack Kaelbling, Jak Kirman, Ann Nicholson Intercausal Reasoning with Uninstantiated Ancestor Nodes, Pages 317-325, Marek J. Druzdzel, Max Henrion Inference Algorithms for Similarity Networks, Pages 326-334, Dan Geiger, David Heckerman Two Procedures for Compiling Influence Diagrams, Pages 335-341, Paul E. Lehner, Azar Sadigh An efficient approach for finding the MPE in belief networks, Pages 342-349, Zhaoyu Li, Bruce D'Ambrosio A Method for Planning Given Uncertain and Incomplete Information, Pages 350-358, Todd Michael Mansell The use of conflicts in searching Bayesian networks, Pages 359-367, David Poole GALGO: A Genetic ALGOrithm Decision Support Tool for Complex Uncertain Systems Modeled with Bayesian Belief Networks, Pages 368-375, Carlos Rojas-Guzmán, Mark A. Kramer Using Tree-Decomposable Structures to Approximate Belief Networks, Pages 376-382, Sumit Sarkar Using Potential Influence Diagrams for Probabilistic Inference and Decision Making, Pages 383-390, Ross D. Shachter, Pierre Ndilikilikesha Deciding Morality of Graphs is NP-complete, Pages 391-399, T.S. Verma, J. Pearl Incremental computation of the value of perfect information in stepwise-decomposable influence diagrams, Pages 400-407, Nevin Zhang Lianwen, Runping Qi, David Poole Argumentative inference in uncertain and inconsistent knowledge bases, Pages 411-419, Salem Benferhat, Didier Dubois, Henri Prade Argument Calculus and Networks, Pages 420-427, Adnan Y. Darwiche Argumentation as a General Framework for Uncertain Reasoning, Pages 428-434, John Fox, Paul Krause, Morten EIvang-Gøransson On reasoning in networks with qualitative uncertainty, Pages 435-442, Simon Parsons, E.H. Mamdani Qualitative Measures of Ambiguity, Pages 443-450, S.K.M. Wong, Z.W. Wang A BAYESIAN VARIANT OF SHAFER'S COMMONALITIES FOR MODELLING UNFORESEEN EVENTS, Pages 453-460, Robert F. Bordley The Probability of a Possibility: Adding Uncertainty to Default Rules, Pages 461-468, Craig Boutilier Possibilistic decreasing persistence, Pages 469-476, Dimiter Driankov, Jérôme Lang DISCOUNTING AND COMBINATION OPERATIONS IN EVIDENTIAL REASONING, Pages 477-484, J.W. Guan, D.A. Bell Probabilistic Assumption-Based Reasoning, Pages 485-491, Jürg Kohlas, Paul-André Monney Partially Specified Belief Functions, Pages 492-499, Serafín Moral, Luis M. de Campos Jeffrey's rule of conditioning generalized to belief functions., Pages 500-505, Philippe SMETS Inference with Possibilistic Evidence, Pages 506-514, Fengming Song, Ping Liang Constructing Lower Probabilities, Pages 515-518, Carl Wagner, Bruce Tonn Belief Revision in Probability Theory, Pages 519-526, Pei Wang The Assumptions Behind Dempster's Rule, Pages 527-534, Nic Wilson A Belief-Function Based Decision Support System, Pages 535-542, Hong Xu, Yen-Teh Hsia, Philippe Smets Author Index, Page 553