Uncertainty In Artificial Intelligence 5 (machine Intelligence And Pattern Recognition) (v. 5)

This volume, like its predecessors, reflects the cutting edge of research on the automation of reasoning under uncertainty. A more pragmatic emphasis is evident, for although some papers address fundamental issues, the majority address practical issues. Topics include the relations between alternative formalisms (including possibilistic reasoning), Dempster-Shafer belief functions, non-monotonic reasoning, Bayesian and decision theoretic schemes, and new inference techniques for belief nets. New techniques are applied to important problems in medicine, vision, robotics, and natural language understanding. Content: Machine Intelligence and Pattern RecognitionPage ii Front MatterPage iii Copyright pagePage iv PrefacePages v-viMax Henrion ReviewersPage xi Program CommitteePage xi ContributorsPages xiii-xiv Lp—A Logic for Statistical InformationPages 3-14Fahiem Bacchus Representing Time in Causal Probabilistic NetworksPages 15-28Carlo BERZUINI Constructing the Pignistic Probability Function in a Context of UncertaintyPages 29-39Philippe SMETS Can Uncertainty Management Be Realized In A Finite Totally Ordered Probability Algebra?Pages 41-57Yang Xiang, Michael P. Beddoes, David Poole Defeasible Reasoning and Uncertainty: CommentsPages 61-66Benjamin N. Grosof Uncertainty and Incompleteness: Breaking the Symmetry of Defeasible Reasoning *Pages 67-85Piero P. Bonissone, David A. Cyrluk, James W. Goodwin, Jonathan Stillman Deciding Consistency of Databases Containing Defeasible and Strict Information*Pages 87-97Moisés Goldszmidt, Judea Pearl Defeasible Decisions: What the Proposal is and Isn'tPages 99-116R.P. Loui Conditioning on Disjunctive Knowledge: Simpson's Paradox in Default LogicPages 117-125Eric Neufeld, J.D. Horton An Introduction to Algorithms for Inference in Belief NetsPages 129-138Max Henrion d-Separation: From Theorems to AlgorithmsPages 139-148Dan Geiger, Thomas Verma, Judea Pearl Interval Influence DiagramsPages 149-161Kenneth W. Fertig, John S. Breese A Tractable Inference Algorithm for Diagnosing Multiple Diseases1Pages 163-171David Heckerman Evidence Absorption and Propagation Through Evidence ReversalsPages 173-190Ross D. Shachter An Empirical Evaluation of a Randomized Algorithm for Probabilistic InferencePages 191-207R. Martin Chavez, Gregory F. Cooper Weighing and Integrating Evidence for Stochastic Simulation in Bayesian NetworksPages 209-219Robert Fung, Kuo-Chu Chang Simulation Approaches to General Probabilistic Inference on Belief NetworksPages 221-231Ross D. Shachter, Mark A. Peot Software tools for uncertain reasoning: An IntroductionPage 235Jack S. Breese Now that I Have a Good Theory of Uncertainty, What Else Do I Need? *Pages 237-253Piero P. Bonissone Knowledge Acquisition Techniques for Intelligent Decision Systems: Integrating Axotl and Aquinas in DDUCKSPages 255-270Jeffrey M. Bradshaw, Stanley P. Covington, Peter J. Russo, John H. Boose BaRT: A Bayesian Reasoning Tool for Knowledge Based SystemsPages 271-282Lashon B. Booker, Naveen Hota, Connie Loggia Ramsey Assessment, criticism and improvement of imprecise subjective probabilities for a medical expert systemPages 285-294David J Spiegelhalter, Rodney C G Franklin, Kate Bull Automated construction of sparse Bayesian networks from unstructured probabilistic models and domain informationPages 295-308Sampath Srinivas, Stuart Russell, Alice Agogino A Decision-Analytic Model for Using Scientific DataPages 309-318Harold P. Lehmann Verbal expressions for probability updates How much more probable is “much more probable”?Pages 319-328Christopher Elsaesser, Max Henrion Map Learning with Indistinguishable LocationsPages 331-341Kenneth Basye, Thomas Dean Plan Recognition in Stories and in Life*Pages 343-351Eugene Charniak, Robert Goldman Hierarchical Evidence Accumulation in the Pseiki System and Experiments in Model-Driven Mobile Robot Navigation*Pages 353-369A.C. Kak, K.M. Andress, C. Lopez-Abadia, M.S. Carroll, J.R. Lewis Model-Based Influence Diagrams For Machine VisionPages 371-388T.S. Levitt, J.M. Agosta, T.O. Binford The Application of Dempster Shafer Theory to a Logic-Based Visual Recognition SystemPages 389-405Gregory M. Provan Efficient Parallel Estimation for Markov Random FieldsPages 407-419Michael J. Swain, Lambert E. Wixson, Paul B. Chou Comparing Approaches to Uncertain Reasoning: Discussion System Condemnation Pays OffPages 423-426Ward Edwards A Probability Analysis of the Usefulness of Decision Aids1Pages 427-436Paul E. Lehner, Theresa M. Mullin, Marvin S. Cohen Inference Policies12Pages 437-444Paul E. Lehner Comparing Expert Systems Built Using Different Uncertain Inference Systems*Pages 445-455David S. Vaughan, Bruce M. Perrin, Robert M. Yadrick, Peter D. Holden Shootout-89, An Evaluation of Knowledge-based Weather Forecasting SystemsPages 457-458W.R. Moninger Author indexPage 459 This volume, like its predecessors, reflects the cutting edge of research on the automation of reasoning under uncertainty.

A more pragmatic emphasis is evident, for although some papers address fundamental issues, the majority address practical issues. Topics include the relations between alternative formalisms (including possibilistic reasoning), Dempster-Shafer belief functions, non-monotonic reasoning, Bayesian and decision theoretic schemes, and new inference techniques for belief nets. New techniques are applied to important problems in medicine, vision, robotics, and natural language understanding.