Explaining by evidence

Given a system description and its observed behaviour, there usually exist several possible explanations of the situation. Different explanations may contradict one another and suggest conflicting actions. So, in order to take proper actions sanctioned by the real situation, we need to single out an explanation that is most adequate to the reality. Existing methods for determining of such a best explanation require an extensive statistical knowledge about the system behaviour, which often is either only partially available or not satisfactorily reliable. Trying to overcome this lack of data, we introduce the notion of evidence as a measure of credibility of beliefs based on the semantic information contained in the system and supplied by the observations. Reasoning by evidence is applied to the process of determining a best abductive explanation. The notion of conjecture is presented as a generalization of explanation.Then reasoning by evidence is applied to testing and refining of diagnoses.

[1]  Eliezer L. Lozinskii,et al.  Information and evidence in logic systems , 1994, J. Exp. Theor. Artif. Intell..

[2]  Noam Nisan,et al.  Approximate Inclusion-Exclusion , 1990, Comb..

[3]  Paul Walton Purdom,et al.  Average Time Analyses of Simplified Davis-Putnam Procedures , 1982, Inf. Process. Lett..

[4]  Georg Gottlob,et al.  Formalizing the repair process , 1992 .

[5]  Philippe Besnard,et al.  Explanatory Diagnoses and their Computation by Circumscription , 1992, ECAI.

[6]  J. Aczél,et al.  Maximum Entropy and Bayesian Methods in Applied Statistics: Generalized Entropies and the Maximum Entropy Principle , 1986 .

[7]  Edward T. Lee,et al.  An algorithm for region filling using two‐dimensional grammars , 1987, Int. J. Intell. Syst..

[8]  Hector J. Levesque,et al.  A Knowledge-Level Account of Abduction , 1989, IJCAI.

[9]  Judea Pearl,et al.  Probabilistic Semantics for Nonmonotonic Reasoning: A Survey , 1989, KR.

[10]  Paul Walton Purdom,et al.  The Pure Literal Rule and Polynomial Average Time , 1985, SIAM J. Comput..

[11]  Stephen A. Cook,et al.  The complexity of theorem-proving procedures , 1971, STOC.

[12]  Gregory M. Provan,et al.  What is the most likely diagnosis? , 1990, UAI.

[13]  M. Tribus.  Information Theory as the Basis for Thermostatics and Thermodynamics , 1961 .

[14]  Raymond Reiter,et al.  Characterizing Diagnoses , 1990, AAAI.

[15]  David Poole,et al.  Representing Diagnostic Knowledge for Probabilistic Horn Abduction , 1991, IJCAI.

[16]  Randall Davis,et al.  Diagnostic Reasoning Based on Structure and Behavior , 1984, Artif. Intell..

[17]  Sheila A. McIlraith.  Generating Tests Using Abduction , 1994, KR.

[18]  Igor Mozetic.  A Polynomial-time Algorithm for Model-Based Diagnosis , 1992, ECAI.

[19]  David Chapman,et al.  Planning for Conjunctive Goals , 1987, Artif. Intell..

[20]  James A. Reggia,et al.  Diagnostic Expert Systems Based on a Set Covering Model , 1983, Int. J. Man Mach. Stud..

[21]  Solomon Eyal Shimony.  Explanation, Irrelevance, and Statistical Independence , 1991, AAAI.

[22]  Nils J. Nilsson,et al.  Probabilistic Logic * , 2022 .

[23]  P. M. Williams.  Bayesian Conditionalisation and the Principle of Minimum Information , 1980, The British Journal for the Philosophy of Science.

[24]  H. E. Pople,et al.  Internist-1, an experimental computer-based diagnostic consultant for general internal medicine. , 1982, The New England journal of medicine.

[25]  Leslie G. Valiant,et al.  The Complexity of Computing the Permanent , 1979, Theor. Comput. Sci..

[26]  Eliezer L. Lozinskii.  A Simple Test Improves Checking Satisfiability , 1993, J. Log. Program..

[27]  Hwee Tou Ng,et al.  Abductive Plan Recognition and Diagnosis: A Comprehensive Empirical Evaluation , 1992, KR.

[28]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[29]  Mill Johannes G.A. Van,et al.  Transmission Of Information , 1961 .

[30]  Steen Andreassen,et al.  MUNIN - A Causal Probabilistic Network for Interpretation of Electromyographic Findings , 1987, IJCAI.

[31]  Michael R. Genesereth,et al.  The Use of Design Descriptions in Automated Diagnosis , 1984, Artif. Intell..

[32]  Solomon Eyal Shimony,et al.  Cost-Based Abduction and MAP Explanation , 1994, Artif. Intell..

[33]  Claudia Böttcher,et al.  No Faults in Structure? How to Diagnose Hidden Interactions , 1995, IJCAI.

[34]  Jerry R. Hobbs,et al.  Interpretation as Abduction , 1993, Artif. Intell..

[35]  R. G. Lacsamana,et al.  Where do we go from here? , 1986, The Journal of the Florida Medical Association.

[36]  Hector J. Levesque,et al.  Abductive and Default Reasoning: A Computational Core , 1990, AAAI.

[37]  David B. Leake.  Focusing Construction and Selection of Abductive Hypotheses , 1993, IJCAI.

[38]  Raymond Reiter,et al.  A Theory of Diagnosis from First Principles , 1986, Artif. Intell..

[39]  Joseph Y. Halpern,et al.  Random Worlds and Maximum Entropy , 1994, J. Artif. Intell. Res..

[40]  Roger C. Schank,et al.  Creativity and Learning in a Case-Based Explainer , 1989, Artif. Intell..

[41]  Adnan Darwiche.  Model-Based Diagnosis using Causal Networks , 1995, IJCAI.

[42]  Eugene Santos,et al.  Dynamic MAP Calculations for Abduction , 1992, AAAI.

[43]  E. Jaynes.  Information Theory and Statistical Mechanics , 1957 .

[44]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems , 1988 .

[45]  Peter Szolovits,et al.  Categorical and Probabilistic Reasoning in Medical Diagnosis , 1990, Artif. Intell..

[46]  Raymond Reiter,et al.  A Logic for Default Reasoning , 1987, Artif. Intell..

[47]  ELIEZER L. LOZINSKII,et al.  Is there an alternative to parsimonious semantics? , 1995, J. Exp. Theor. Artif. Intell..

[48]  Dan Roth,et al.  On the Hardness of Approximate Reasoning , 1993, IJCAI.

[49]  Olivier Dubois,et al.  Counting the Number of Solutions for Instances of Satisfiability , 1991, Theor. Comput. Sci..

[50]  Wenhui Zhang,et al.  Number of Models and Satisfiability of Sets of Clauses , 1996, Theor. Comput. Sci..

[51]  Kave Eshghi,et al.  Abductive Planning with Event Calculus , 1988, ICLP/SLP.

[52]  Kazuo Iwama,et al.  CNF Satisfiability Test by Counting and Polynomial Average Time , 1989, SIAM J. Comput..

[53]  David Heckerman,et al.  Probabilistic Interpretation for MYCIN's Certainty Factors , 1990, UAI.

[54]  Hilary Putnam,et al.  A Computing Procedure for Quantification Theory , 1960, JACM.

[55]  Ramesh S. Patil,et al.  Causal representation of patient illness for electrolyte and acid-base diagnosis , 1981 .

[56]  Joe W. Duran,et al.  A General Expert System Design for Diagnostic Problem Solving , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[57]  Giorgio Gallo,et al.  Algorithms for Testing the Satisfiability of Propositional Formulae , 1989, J. Log. Program..

[58]  John McCarthy,et al.  Circumscription - A Form of Non-Monotonic Reasoning , 1980, Artif. Intell..

[59]  Bon K. Sy,et al.  Reasoning MPE to Multiply Connected Belief Networks Using Message Passing , 1992, AAAI.

[60]  Kave Eshghi,et al.  A Tractable Class of Abduction Problems , 1993, IJCAI.

[61]  H. E. Pople,et al.  Internist-I, an Experimental Computer-Based Diagnostic Consultant for General Internal Medicine , 1982 .

[62]  Brian C. Williams,et al.  Diagnosing Multiple Faults , 1987, Artif. Intell..

[63]  Eliezer L. Lozinskii.  Counting Propositional Models , 1992, Inf. Process. Lett..

[64]  Yun Peng,et al.  A Probabilistic Causal Model for Diagnostic Problem Solving Part I: Integrating Symbolic Causal Inference with Numeric Probabilistic Inference , 1987, IEEE Transactions on Systems, Man, and Cybernetics.