Uncertainty in artificial intelligence

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書誌事項

Uncertainty in artificial intelligence

edited by Laveen N. Kanal and John F. Lemmer

（Machine intelligence and pattern recognition, v. 4, 5, 8, 9, 10, 12）

North-Holland, 1986-

  • 1 : hard
  • 1 : pbk
  • 2
  • 3
  • 4 : hard
  • 4 : pbk
  • 5 : hard
  • 5 : pbk
  • 6

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注記

3 / edited by Laveen N. Kanal, Tod S. Levitt, John F. Lemmer

4 / edited by Ross D. Shachter ... [et al.]

5 / edited by Max Henrion ... [et al.]

6 / edited by Piero P. Bonissone ... [et al.]

内容説明・目次

巻冊次

1 : hard ISBN 9780444700582

内容説明

How to deal with uncertainty is a subject of much controversy in Artificial Intelligence. This volume brings together a wide range of perspectives on uncertainty, many of the contributors being the principal proponents in the controversy. Some of the notable issues which emerge from these papers revolve around an interval-based calculus of uncertainty, the Dempster-Shafer Theory, and probability as the best numeric model for uncertainty. There remain strong dissenting opinions not only about probability but even about the utility of any numeric method in this context.

目次

Overviews and Reviews (C. Berenstein, R.K. Bhatnagar, L.N. Kanal and D. Lavine). Explication or Critique of Current Approaches to Uncertainty (P.P. Bonissone, B. Chandrasekaran, P. Cheeseman, K.S. Decker, B.N. Grosof, D. Heckerman, M. Henrion, E. Horvitz, D. Hunter, R.W. Johnson, J.F. Lemmer, G. Shafer, J.E. Shore, D.J. Spiegelhalter, M.C. Tanner, B.P. Wise, L.A. Zadeh and A.C. Zimmer). Synthesis of Current Approaches to Uncertainty (C.Y. Chong, M.S. Cohen, R.M. Fung, G.S.-H. Liu and R.R. Yager). Incorporating Uncertainty in Systems (J. Breese, M.L. Ginsberg, P.D. Holden, S. Holtzman, K.G. Kempf, T.S. Levitt, J. Pearl, B.M. Perrin, R.D. Shachter, D.S. Vaughan and R.M. Yadrick). Techniques for Inducing and Processing Uncertain Information (M. Bauer, N.C. Dalkey, H. Hamburger, S.J. Hanson and L. Rendell). Alternate Perspectives (J. Fox, R.P. Loui and R. Solomonoff). Alternatives to Minimax in Game Playing (B. Abramson, D. Nau, P. Purdom and C.-H. Tzeng).
巻冊次

2 ISBN 9780444703965

内容説明

This second volume is arranged in four sections: Analysis contains papers which compare the attributes of various approaches to uncertainty. Tools provides sufficient information for the reader to implement uncertainty calculations. Papers in the Theory section explain various approaches to uncertainty. The Applications section describes the difficulties involved in, and the results produced by, incorporating uncertainty into actual systems.

目次

Analysis. Models vs. Inductive Inference for Dealing with Probabilistic Knowledge (N.C. Dalkey). An Axiomatic Framework for Belief Updates (D.E. Heckerman). The Myth of Modularity in Rule-Based Systems for Reasoning with Uncertainty (D.E. Heckerman, E.J. Horvitz). Imprecise Meanings as a Cause of Uncertainty in Medical Knowledge-Based Systems (S.J. Henkind). Evidence as Opinions of Experts (R. Hummel, M. Landy). Probabilistic Logic: Some Comments and Possible Use for Nonmonotonic Reasoning (M. McLeish). Experiments with Interval-Valued Uncertainty (R.M. Tong, L.A. Appelbaum). Evaluation of Uncertain Inference Models I: PROSPECTOR (R.M. Yadrick et al.). Experimentally Comparing Uncertain Inference Systems to Probability (B.P. Wise). Tools. Knowledge Engineering within a Generalized Bayesian Framework (S.W. Barth, S.W. Norton). Learning to Predict: An Inductive Approach (K. Chen). Towards a General Purpose Belief Maintenance System (B. Falkenhainer). A Non-Iterative Maximum Entropy Algorithm (S.A. Goldman, R.L. Rivest). Propagating Uncertainty in Bayesian Networks by Probabilistic Logic Sampling (M. Henrion). An Explanation Mechanism for Bayesian Inferencing Systems (S.W. Norton). On the Rational Scope of Probabilistic Rule-Based Inference Systems (S. Schocken). DAVID: Influence Diagram Processing System for the Macintosh (R.D. Shachter). Qualitative Probabilistic Networks for Planning under Uncertainty (M.P. Wellman). On Implementing Usual Values (R.R. Yager). Theory. Some Extensions of Probabilistic Logic (S.-S. Chen). Belief as Summarization and Meta-Support (A.J. Craddock, R.A. Browse). Non-Monotonicity in Probabilistic Reasoning (B.N. Grosof). A Semantic Approach to Non-Monotonic Entailment (J. Hawthorne). Knowledge (H.E. Kyburg, Jr.). Computing Reference Classes (R.P. Loui). Distributed Revision of Belief Commitment in Composite Explanations (J. Pearl). A Backwards View for Assessment (R.D. Schachter, D. Heckerman). Propagation of Belief Functions: A Distributed Approach (P.P. Shenoy, G. Shafer, K. Mellouli). Generalizing Fuzzy Logic Probabilistic Inferences (S. Ursic). Applications. The Sum-and-Lattice-Points Method Based on an Evidential-Reasoning System Applied to Real-Time Vehicle Guidance (S. Abel). Probabilistic Reasoning About Ship Images (L.B. Booker, N. Hota). Information and Multi-Sensor Coordination (G. Hager, H.F. Durrant-Whyte). Planning, Scheduling, and Uncertainty in the Sequence of Future Events (B.R. Fox, K.G. Kempf). Evidential Reasoning in a Computer Vision System (Z.-N. Li, L. Uhr). Bayesian Inference for Radar Imagery Based Surveillance (T.S. Levitt). A Causal Bayesian Model for the Diagnosis of Appendicitis (S.M. Schwartz, J. Baron, J.R. Clarke). Estimating Uncertain Spatial Relationships in Robotics (R. Smith, M. Self, P. Cheeseman).
巻冊次

3 ISBN 9780444874177

内容説明

Being a subject of much controversy in Artificial Intelligence, there are still many open fundamental issues in the area of uncertain reasoning. This third volume presents new research results, representing significant progress in a number of issues. Because representation and reasoning under uncertainty are still poorly understood, and because implementation choices and tradeoffs are best understood in the context of specific applications, most of the papers address multiple issues in uncertain reasoning. They are divided into four categories: Interpretation and comparison of uncertainty calculi; Representation and computation in Bayesian inference; Structure and control for systems reasoning under uncertainty; and Learning and explanation.

目次

Interpretation and Comparison. An Algorithm for Computing Probabilistic Propositions (G.F. Cooper). Higher Order Probabilities (H.E. Kyburg). Nilsson's Probabilistic Entailment Extended to Dempster-Shafer Theory (M. McLeish). Towards Solving the Multiple Extension Problem: Combining Defaults and Probability (E. Neufeld, D. Poole). Satisfaction of Assumptions is a Weak Predictor of Performance (B.P. Wise). Evaluation of Uncertain Inference Models III: The Role of Tuning (B.P. Wise et al.). Can Evidence be Combined in the Dempster-Shafer Theory (J. Yen). Representation and Computation in Bayesian Inference. Bayesian Inference in Model-Based Machine Vision (T.O. Binford, T.S. Levitt, W.B. Mann). Computing Belief Commitments Using Tensor Products (L.B. Booker, N. Hota, G. Hemphill). Decision Tree Induction Systems: A Bayesian Analysis (W.L. Buntine). Bayesian Belief Network Inference Using Stimulation (H.L. Chin, G.F. Cooper). A Bayesian Perspective on Confidence (D. Heckerman, H. Jimison). Some Practical Issues in Constructing Belief Networks (M. Henrion). The Recovery of Causal Poly-Trees from Statistical Data (G. Rebane, J. Pearl). A Heuristic Bayesian Approach to Knowledge Acquisition: Application to the Analysis of Tissue-Type Plasminogen Activator (R.D. Shachter et al.). Advantages and a Limitation of Using LEG Nets in a Real-Time Problem (T.B. Slack). A Unified Approach to Impression and Sensitivity of Beliefs in Expert Systems (D.J. Spiegelhalter). Structuring Causal Tree Models with Continuous Variables (L. Xu, J. Pearl). Structure and Control. Using the Dempster-Shafer Scheme in a Mixed-Initiative Expert System Shell (G. Biswas, T.S. Anand). T-norm Based Reasoning in Situation Assessment Applications (P.S. Bonissone, N.C. Wood). Steps Towards Programs that Manage Uncertainty (P.R. Cohen). A Hybrid Approach to Reasoning Under Uncertainty (B. D'Ambrosio). Estimation Procedures for Robust Sensor Control (G. Hager, M. Mintz). Reasoning About Beliefs and Actions Under Computational Resource Constraints (E.J. Horvitz). Efficient Inference on Generalized Fault Diagrams (R.D. Shachter, L.J. Bertrand). Implementing Evidential Reasoning in Expert Systems (J. Yen). Learning and Explanation. The Automatic Training of Rule Bases that use Numerical Uncertainty Representations (R.A. Caruana). Modifiable Combining Functions (P.R. Cohen, G. Shafer, P.P. Shenoy). The Inductive Logic of Information Systems (N. Dalkey). Explanation of Probabilistic Inference (C. Elsaesser). Theory-Based Inductive Learning: An Integration of Symbolic and Quantitative Methods (S. Star).
巻冊次

4 : hard ISBN 9780444886507

内容説明

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.

目次

I. Causal Models. On the Logic of Causal Models (D. Geiger, J. Pearl). Process, Structure, and Modularity in Reasoning with Uncertainty (B. D'Ambrosio). Probabilistic Causal Reasoning (T. Dean, K. Kanazawa). Generating Decision Structures and Causal Explanations for Decision Making (S. Star). Control of Problem Solving: Principles and Architecture (J.S. Breese, M.R. Fehling). Causal Networks: Semantics and Expressiveness (T. Verma, J. Pearl). II. Uncertainty Calculi and Comparisons. 1. Uncertainty Calculi. Stochastic Sensitivity Analysis Using Fuzzy Influence Diagrams (P. Jain, A.M. Agogino). A Linear Approximation Method for Probabilistic Inference (R.D. Shachter). Minimum Cross Entropy Reasoning in Recursive Causal Networks (W.X. Wen). Probabilistic Semantics and Defaults (E. Neufeld, D. Poole, R. Aleliunas). Modal Logics of Higher-Order Probability (P. Haddawy, A.M. Frisch). A General Non-Probabilistic Theory of Inductive Reasoning (W. Spohn). Epistemological Relevance and Statistical Knowledge (H.E. Kyburg, Jr.). Axioms for Probability and Belief-Function Propagation (P.F. Shenoy, G. Shafer). A Summary of a New Normative Theory of Probabilistic Logic (R. Aleliunas). Hierarchical Evidence and Belief Functions (P.K. Black, K.B. Laskey). On Probability Distributions over Possible Worlds (F. Bacchus). A Framework of Fuzzy Evidential Reasoning (J. Yen). 2. Comparisons. Parallel Belief Revision (D. Hunter). Evidential Reasoning Compared in a Network Usage Prediction Testbed: Preliminary Report (R.P. Loui). A Comparison of Decision Analysis and Expert Rules for Sequential Diagnosis (J. Kalagnanam, M. Henrion). An Empirical Comparison of Three Inference Methods (D. Heckerman). Modeling Uncertain and Vague Knowledge in Possibility and Evidence Theories (D. Dubois, H. Prade). Probabilistic Inference and Non-Monotonic Inference (H.E. Kyburg, Jr.). Multiple Decision Trees (S.W. Kwok, C. Carter). III. Knowledge Acquisition and Explanation. KNET: Integrating Hypermedia and Normative Bayesian Modeling (R.M. Chavez, G.F. Cooper). Generating Explanations of Decision Models Based on an Augmented Representation of Uncertainty (H.B. Jimison). IV. Applications. Induction and Uncertainty Management Techniques Applied to Veterinary Medical Diagnosis (M. Cecile, M. McLeish, P. Pascoe, W. Taylor). Predicting the Likely Behaviors of Continuous Nonlinear Systems in Equilibrium (A. Yeh). The Structure of Bayes Networks for Visual Recognition (J.M. Agosta). Utility-Based Control for Computer Vision (T.S. Levitt, T.O. Binford, G.J. Ettinger).
巻冊次

5 : hard ISBN 9780444887382

内容説明

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.

目次

Fundamental Issues. Defeasible Reasoning and Uncertainty. Algorithms for Inference in Belief Nets. Software Tools for Uncertain Reasoning. Knowledge Acquisition, Modelling, and Explanation. Applications to Vision and Recognition. Comparing Approaches to Uncertain Reasoning. Author Index.
巻冊次

6 ISBN 9780444892645

内容説明

This collection of papers reflects the international participation that is becoming typical of the Conference on Uncertainty and AI. Increasing contributions from Canadian, European, and Australian researchers have enriched the content of the present proceedings, and have led to the development of broader perspectives and deeper exchanges of technical ideas and experiences. This book comprises such topics as: the relations between alternative formalisms, including possibilistic reasoning, Dempster Shafer belief functions, non-monotonic reasoning, Bayesian and decision theoretic schemes; new inference techniques for belief nets; and applications of new techniques to important problems in medicine, vision, robotics and natural language understanding. An important edition for industrial and technical libraries, this volume should not be missed by developers of AI systems, university and industrial researchers, or students and faculty in the field of AI.

目次

Qualitative Probabilistic Reasoning and Cognitive Models. Exploiting Functional Dependencies in Qualitative Probabilistic Reasoning (M.P. Wellman). Qualitative Propagation and Scenario-Based Scheme for Explaining Probabilistic Reasoning (M. Henrion, M.J. Druzdel). Propagating Uncertainty in Rule Based Cognitive Modeling (T.R. Shultz). Context-Dependent Similarity (Y. Cheng). Abductive Probabilistic Reasoning and KB Development. Similarity Networks for the Construction of Multiple-Faults Belief Networks (D. Heckerman). Separable and Transitive Graphoids (D. Geiger, D. Heckerman). Integrating Probabilistic, Taxonomic and Causal Knowledge in Abductive Diagnosis (D. Lin, R. Goebel). What is the Most Likely Diagnosis (D. Poole, G.M. Provan). Probabilistic Evaluation of Candidate Sets for Multidisorder Diagnosis (T.D. Wu). Kutato: An Entropy-Driven System for Construction of Probabilistic Expert Systems from Databases (E. Herskovits, G. Cooper). Problem Formulation and Control of Reasoning. Ideal Reformulation of Belief Networks (J.S. Breese, E.J. Horvitz). Computationally-Optimal Real-Resource Strategies for Independent, Uninterruptible Methods (D. Einav, M.R. Fehling). Problem Formulation as the Reduction of a Decision Model (D.E. Heckerman, E.J. Horvitz). Dynamic Construction of Belief Networks (R.P. Goldman, E. Charniak). A New Algorithm for Finding MAP Assignments to Belief Networks (S.E. Shimony, E. Charniak). Belief Network Decomposition. Directed Reduction Algorithms and Decomposable Graphs (R.D. Shachter, S.K. Andersen, K.L. Poh). Optimal Decomposition of Belief Networks (W.X. Wen). Pruning Bayesian Networks for Efficient Computation (M. Baker, T.E. Boult). On Heuristics for Finding Loop Cutsets in Multiply-Connected Belief Networks (J. Stillman). A Combination of Cutset Conditioning with Clique-Tree Propagation in the Pathfinder System (H.J. Suermondt, G.F. Cooper, D.E. Heckerman). Equivalence and Synthesis of Causal Models (T.S. Verma, J. Pearl). Possibility Theory: Semantics and Applications. Possibility as Similarity: The Semantics of Fuzzy Logic (E. Ruspini). Integrating Case-Based and Rule-Based Reasoning: the Possibilistic Connection (S. Dutta, P.P. Bonissone). Credibility Discounting in the Theory of Approximate Reasoning (R.R. Yager). Updating with Belief Functions, Ordinal Conditional Functions and Possibility Measures (D. Dubois, H. Prade). A Hierarchical Approach to Designing Approximate Reasoning-Based Controllers for Dynamic Physical Systems (H.R. Berenji, et al.). Dempster-Shafer: Graph Decomposition, FMT, and Interpretations. A New Approach to Updating Beliefs (R. Fagin, J.Y. Halpern). The Transferable Belief Model and Other Interpretations of Dempster-Shafer's Model (P. Smets). Valuation-Based Systems for Discrete Optimization (P.P. Shenoy). Computational Aspects of the Mobius Transformation (R. Kennes, P. Smets). Using Dempster-Shafer Theory in Knowledge Representation (A. Saffiotti).

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