Topological framework for representing and solving probabilistic inference problems in expert systems

The authors present the concept of influence diagrams for representing probabilistic dependence and independence between state variables in a given problem domain and a topological framework for solving probabilistic inference problems in expert systems. The mathematical basis for influence diagrams is explained and theorems for mathematical manipulation of them are presented, in a graph-theoretic framework. Topological transformation rules developed in previous research are formalized in an axiomatic manner based on a concept of consistency. A polynomial-time symbolic-level algorithm for solving probabilistic inference problems is developed. The algorithm involves searching through the diagram to answer any specific diagnostic query about the system. >

[1]  Alice M. Agogino,et al.  Multiple sensor expert system for diagnostic reasoning, monitoring and control of mechanical systems , 1988 .

[2]  Ross D. Shachter,et al.  A heuristic Bayesian approach to knowledge acquisition: Application to analysis of tissue-type plasminogen activator , 1987, Int. J. Approx. Reason..

[3]  Piero P. Bonissone,et al.  Editorial: Reasoning with Uncertainty in Expert Systems , 1985, Int. J. Man Mach. Stud..

[4]  Judea Pearl,et al.  Fusion, Propagation, and Structuring in Belief Networks , 1986, Artif. Intell..

[5]  Michael L. Donnell,et al.  Building decision aids: Exploiting the synergy between decision analysis and artificial intelligence , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[6]  RICHARD 0. DUDA,et al.  Subjective bayesian methods for rule-based inference systems , 1899, AFIPS '76.

[7]  L. Zadeh.  The role of fuzzy logic in the management of uncertainty in expert systems , 1983 .

[8]  Judea Pearl,et al.  A Computational Model for Causal and Diagnostic Reasoning in Inference Systems , 1983, IJCAI.

[9]  Lawrence M. Fagan,et al.  Using Decision Theory to Justify Heuristics , 1986, AAAI.

[10]  Andrew P. Sage,et al.  Aiding the Human Decisionmaker through the Knowledge-Based Sciences , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[11]  Max Henrion,et al.  An Experimental Comparison of Knowledge Engineering for Expert Systems and for Decision Analysis , 1987, AAAI.

[12]  A. Dawid.  Conditional Independence in Statistical Theory , 1979 .

[13]  Judea Pearl,et al.  Structuring causal trees , 1986, J. Complex..

[14]  Ross D. Shachter.  Intelligent Probabilistic Inference , 1985, UAI.

[15]  Alice M. Agogino,et al.  IDES: influence diagram based expert system , 1987 .

[16]  Ross D. Shachter.  Evaluating Influence Diagrams , 1986, Oper. Res..

[17]  T. Speed,et al.  Markov Fields and Log-Linear Interaction Models for Contingency Tables , 1980 .

[18]  Edward H. Shortliffe,et al.  A model of inexact reasoning in medicine , 1990 .

[19]  Eric Horvitz,et al.  Reasoning about beliefs and actions under computational resource constraints , 1987, Int. J. Approx. Reason..

[20]  Gregory F. Cooper,et al.  Stochastic simulation of Bayesian belief networks , 1987, Int. J. Approx. Reason..

[21]  Max Henrion,et al.  Practical issues in constructing a Bayes belief network , 1987, Int. J. Approx. Reason..

[22]  Richard E. Barlow,et al.  Using Influence Diagrams to Solve a Calibration Problem , 1987 .

[23]  Ronald A. Howard,et al.  Bayesian Decision Models for System Engineering , 1965, IEEE Trans. Syst. Sci. Cybern..

[24]  C. N. Liu,et al.  Approximating discrete probability distributions with dependence trees , 1968, IEEE Trans. Inf. Theory.

[25]  Eric Horvitz,et al.  On the Expressiveness of Rule-based Systems for Reasoning with Uncertainty , 1987, AAAI.