Uncertainty in Artificial Intelligence, 4 (Machine Intelligence and Pattern Recognition Series, Vol 9)

Clearly illustrated in this volume is the current relationship between Uncertainty and AI. It has been said that research in AI revolves around five basic questions asked relative to some particular domain: What knowledge is required? How can this knowledge be acquired? How can it be represented in a system? How should this knowledge be manipulated in order to provide intelligent behavior? How can the behavior be explained? In this volume, all of these questions are addressed. From the perspective of the relationship of uncertainty to the basic questions of AI, the book divides naturally into four sections which highlight both the strengths and weaknesses of the current state of the relationship between Uncertainty and AI. Content: Machine Intelligence and Pattern RecognitionPage ii Front MatterPage iii Copyright pagePage iv PrefacePages v-viRoss D. Shachter, Laveen N. Kanal, Tod S. Levitt, John F. Lemmer List of ContributorsPages xi-xii On the Logic of Causal Models*Pages 3-14Dan GEIGER, Judea PEARL Process, Structure, and Modularity in Reasoning with UncertaintyPages 15-25Bruce D'Ambrosio Probabilistic Causal ReasoningPages 27-42Thomas Dean, Keiji Kanazawa Generating Decision Structures and Causal Explanations For Decision MakingPages 43-57Spencer Star Control of Problem Solving: Principles and ArchitecturePages 59-68John S. BREESE, Michael R. FEHLING Causal Networks: Semantics and Expressiveness*Pages 69-76Thomas VERMA, Judea PEARL Stochastic Sensitivity Analysis Using Fuzzy Influence DiagramsPages 79-92Pramod JAIN, Alice M. AGOGINO A Linear Approximation Method for Probabilistic InferencePages 93-103Ross D. SHACHTER Minimum Cross Entropy Reasoning in Recursive Causal Networks1Pages 105-119W.X. Wen Probabilistic Semantics and DefaultsPages 121-131Eric Neufeld, David Poole, Romas Aleliunas Modal Logics of Higher-Order Probability*Pages 133-148Peter Haddawy, Alan M. Frisch A General Non-Probabilistic Theory of Inductive ReasoningPages 149-158Wolfgang SPOHN Epistemological Relevance and Statistical Knowledge*Pages 159-168Henry E. KYBURG Jr. Axioms for Probability and Belief-Function PropagationPages 169-198Prakash P. SHENOY, Glenn SHAFER A Summary of A New Normative Theory of Probabilistic LogicPages 199-206Romas Aleliunas Hierarchical Evidence and Belief FunctionsPages 207-215Paul K. Black, Kathryn B. Laskey On Probability Distributions Over Possible WorldsPages 217-226Fahiem Bacchus A Framework of Fuzzy Evidential ReasoningPages 227-239John Yen Parallel Belief RevisionPages 241-251Daniel Hunter Evidential Reasoning Compared in a Network Usage Prediction Testbed: Preliminary ReportPages 253-269Ronald P. Loui A Comparison of Decision Analysis and Expert Rules for Sequential DiagnosisPages 271-281Jayant Kalagnanam, Max Henrion An Empirical Comparison of Three Inference Methods1Pages 283-302David Heckerman Modeling Uncertain and Vague Knowledge in Possibility and Evidence TheoriesPages 303-318Didier DUBOIS, Henri PRADE Probabilistic Inference and Non-Monotonic Inference*Pages 319-326Henry E. KYBURG Jr. Multiple decision treesPages 327-335Suk Wah Kwok, Chris Carter KNET: Integrating Hypermedia and Normative Bayesian ModelingPages 339-349R. Martin Chavez, Gregory F. Cooper Generating Explanations of Decision Models Based on an Augmented Representation of UncertaintyPages 351-365Holly B. Jimison Induction and Uncertainty Management Techniques Applied to Veterinary Medical DiagnosisPages 369-381M. CECILE, M. MCLEISH, P. PASCOE, W. TAYLOR Predicting the Likely Behaviors of Continuous Nonlinear Systems in EquilibriumPages 383-395Alexander Yeh The structure of Bayes networks for visual recognitionPages 397-405John Mark Agosta Utility-Based Control for Computer VisionPages 407-422Tod S. Levitt, Thomas O. Binford, Gil J. Ettinger Clearly illustrated in this volume is the current relationship between Uncertainty and AI.

It has been said that research in AI revolves around five basic questions asked relative to some particular domain: What knowledge is required? How can this knowledge be acquired? How can it be represented in a system? How should this knowledge be manipulated in order to provide intelligent behavior? How can the behavior be explained? In this volume, all of these questions are addressed. From the perspective of the relationship of uncertainty to the basic questions of AI, the book divides naturally into four sections which highlight both the strengths and weaknesses of the current state of the relationship between Uncertainty and AI.