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 44–54, Morgan Kaufmann [A useful and clearly presented paper that outlines the conditions under which abductive inference is tractable]

Clancey WJ, 1985. “Heuristic classification” Artificial Intelligence, 27289–350 [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 behavior” Artificial Intelligence24347–410 [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 TMS” Artificial Intelliegence28127–162 [The main paper on ATMS. It is neither clear not concise, but it is important]

de Kleer J and Williams BC, 1987. “Diagnosing multiple faults” Artificial Intelligence3297–130 [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 diagnosis” Artificial Intelligence24411–436 [Describes one of the first logic-based diagnosis programs]

Ginsberg M, 1986. “Counterfactuals” Artificial Intelligence3035–80 [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 89–94, 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. 210–214, American Association for Artificial Intelligence [A convincing theoretical account of the combination of ATMS and Dempster–Shafer theory]

McDermott D, 1987. “A critique of pure reason” Computational Intelligence3151–160 [McDermott's now (in)famous attack on logic-based problem solving: still food for thought]

Overbeek R and Lusk E, 1984. “The automated reasoning system ITP—user's manual” Technical Report ANL-84–27, Argonne National Laboratory [The theorem prover upon which Smith's implementation of Reiter's theory was based, chosen partly for its effective treatment of equational theories]

Pearl J, 188, Probabilistic reasoning in intelligent systems: Networks of plausible inference, Los Altos, California: Morgan Kaufmann [An extended account of Bayesian belief updating, including its relation to nonmonotonic logic]

Peng Y and Reggia JA, 1986. “Plausibility of diagnostic hypotheses: The nature of simplicity” In: Proceedings of the 6th National Conference on Artificial Intelligence,140–145, American Association for Artificial Intelligence [Describes the incorporation of probabilistic reasoning into the Generalized Set Covering model of diagnosis]

Poole D, 1988a. “A logical framework for default reasoning” Artificial Intelligence3627–47 [Proposes an account of nonmonotonic reasoning in terms of scenarios, and relates it to default logic. This forms the basís of the Theorist framework]

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 HEJr, 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 1030–1037, 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 547–552, 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]

Reiter R, 1980. “A logic for default reasoning” Artificial Intelligence3181–132 [A thorough and well-written account of default logic that has required little or no revision over the years]

Reiter R, 1987a. “Nonmonotonic reasoning” Annual Reviews of Computer Science2147–186 [A useful overview of nonmonotonic logic]

Reiter R, 1987b. “A theory of diagnosis from first principles” Artificial Intelligence3257–95 [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 programs” Artificial Intelligence21285–325 [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. 89–93, American Association for Artificial Intelligence [Contains a convincing critique of Ginsberg's construction for counterfactual reasoning]