Uncertainty in Artificial Intelligence 2: Volume 5(Volume 5 Machine Intelligence and Pattern Recognition)

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.

Table of Contents:
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).