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Applications of nonmonotonic logic to diagnosis

  • Peter Jackson (a1)


This paper attempts to assess the practical utility of nonmonotonic logic in diagnostic problem solving. We begin with a brief review of the main assumptions which motivate work in this area, and discuss two logic-based approaches which involve nonmonotonic arguments. Then we consider two recent proposals for the application of default logic to diagnosis, as well as a proposal based on counterfactual logic. In conclusion, we briefly compare these methods with other diagnostic reasoning paradigms found in the Artificial Intelligence literature.



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Buchanan, BG and Shortliffe, EH eds, 1984. Rule-based expert systems, Reading, Massachusetts: Addison-Wesley [A collection of seminal papers on expert systems by researchers at Stanford University]
Bylander, T, Allemang, D, Tanner, MC and Josephson, JR, 1989. Some results concerning the computational complexity of abduction” In: Proceedings of the 1st International Conference on Principles of Knowledge Representation and Reasoning, pp 4454, Morgan Kaufmann [A useful and clearly presented paper that outlines the conditions under which abductive inference is tractable]
Clancey, WJ, 1985. “Heuristic classificationArtificial Intelligence, 27 289350 [A theoretical paper that attempts to reconstruct the problem solving paradigm employed by many rule-based expert systems]
Console, L, Dupré, DT and Torasso, P, in press, “A theory of diagnosis for incomplete causal models” To appear in: Proceedings of the 11th International Joint Conference on Artificial Intelligence,Detroit Michigan,August 1989 [A formal treatment of causal reasoning with incomplete knowledge that makes interesting connections with nonmonotonic logic]
Davis, R, 1984. “Diagnostic reasoning based on structure and behaviorArtificial Intelligence 24 347410 [An influential paper on electronic troubleshooting from first principles. It is not reviewed here because it makes no explicit connections with nonmonotonic logic. It is well worth reading, nonetheless]
de Kleer, J, 1986. “An assumption-based TMSArtificial Intelliegence 28 127162 [The main paper on ATMS. It is neither clear not concise, but it is important]
de Kleer, J and Williams, BC, 1987. “Diagnosing multiple faultsArtificial Intelligence 32 97130 [A difficult but important paper that repays study. If you read the version in Ginsberg (1987), note that the columns of text on page 382 are the wrong order!]
Eshelman, L, 1988. “MOLE: A knowledge acquisition tool for cover-and-differentiate systems” Chapter 3 of Marcus, S ed., Automating knowledge acquisition for expert systems, Boston, Massachusetts: Kluwer Academic [Describes a knowledge acquisition tool for systems that use a form of heuristic classification. MOLE is interesting because it reasons explicitly about the space of possible explanations]
Genesereth, MR, 1984. “The use of design descriptions in automated diagnosisArtificial Intelligence 24 411436 [Describes one of the first logic-based diagnosis programs]
Ginsberg, M, 1986. “CounterfactualsArtificial Intelligence 30 3580 [A wide-ranging treatment of counterfactuals and their relevance to AI applications, such as diagnosis and planning]
Ginsberg, M, 1987. Readings in nonmonotonic reasoning, Los Altos, California: Morgan Kaufmann [A good collection of important papers on nonmonotonic logic at a price you can afford]
Jackson, P, 1989. “Prepositional abductive logic” In: Proceedings of the 7th Conference on Artificial Intelligence and the Simulation of Behaviour, pp 8994, London:Pitman [An attempt to provide a proof theory and semantics for abductive inference]
Kahn, G, 1988. “MORE: From observing knowledge engineers to automating knowledge acquisition” Chapter 2 of Marcus, S, ed., Automating knowledge acquisition for expert systems, Boston Masschusetts: Kluwer Academic [An attempt to automate the acquisition of diagnostic knowledge using causal models]
Laskey, K and Lehner, PE, 1988. “Belief maintenance: An integrated approach to uncertainty management” In: Proceedings of the 7th National Conference on Artificial Intelligence, pp. 210214, American Association for Artificial Intelligence [A convincing theoretical account of the combination of ATMS and Dempster–Shafer theory]
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Peng, Y and Reggia, JA, 1986. “Plausibility of diagnostic hypotheses: The nature of simplicity” In: Proceedings of the 6th National Conference on Artificial Intelligence,140145, American Association for Artificial Intelligence [Describes the incorporation of probabilistic reasoning into the Generalized Set Covering model of diagnosis]
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Poole, D, 1988b. “Representing knowledge for logic-based diagnosis” In: Proceedings of the International Conference on Fifth Generation Computer Systems,Tokyo, Japan [Argues that the Theorist framework can be used effectively to compare abduction, diagnosis from first principles, and rule-based diagnosis]
Poole, D, Goebel, R and Aleliunas, R, 1987. “Theorist: A logical reasoning system for defaults and diagnosis” Chapter 13 of Cercone, N and McCalla, G, eds, The knowledge frontier, New York: Springer-Verlag [A somewhat sketchy account of the Theorist framework]
Pople, HE Jr, 1977. “The formation of composite hypotheses in diagnostic problem solving: An exercise in synthetic reasoning” In: Proceedings of the 5th International Joint Conference on Artificial Intelligence, pp 10301037, American Association for Artificial Intelligence [An early paper on abductive inference in AI; still worth reading today]
Provan, GM, 1988. “Solving diagnostic problems using extended truth maintenance systems” In: Proceedings of the 8th European Conference on Artificial Intelligence, pp 547552, London:Pitman [An excellent paper that addresses the difficult problem of integrating truth maintenance and Dempster–Shafer belief functions. A more computational treatment than Laskey and Lehner]
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Reiter, R, 1987a. “Nonmonotonic reasoningAnnual Reviews of Computer Science 2 147186 [A useful overview of nonmonotonic logic]
Reiter, R, 1987b. “A theory of diagnosis from first principlesArtificial Intelligence 32 5795 [A clear theoretical account of diagnoses based on the minimization of abnormality in a system description]
Shoham, Y, 1988. Reasoning about change: Time and causation from the standpoint of artificial intelligence, Cambridge, Massachusetts: MIT Press [An attempt to integrate nonmonotonic logic and temporal logic for reasoning about change]
Singh, N, 1987. An artificial intelligence approach to test generation, Norwell, Massachusetts: Kluwer Academic [Describes some further work on the DART system]
Smith, BA, 1988. “A system for the diagnosis of faults using a first principles approach” PhD thesis, Department of Computer Science, University of Missouri-Rolla [An interesting account of an implementation of Reiter's theory of diagnosis. Also contains excellent review chapters]
Swartout, WR, 1983. “XPLAIN: a system for creating and explaining expert consulting programsArtificial Intelligence 21 285325 [Describes the derivation of an expert system from a domain model by automatic programming]
Winslett, M, 1988. “Reasoning about action using a possible models approach” In: Proceedings of the 7th National Conference on Artificial Intelligence, pp. 8993, American Association for Artificial Intelligence [Contains a convincing critique of Ginsberg's construction for counterfactual reasoning]

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Applications of nonmonotonic logic to diagnosis

  • Peter Jackson (a1)


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