ELSEVIER Expert Systems with Applications 14 (1998) 83-90 Expert Systems with Applications Abduction, that ubiquitous form of reasoning Ram6n Brena* Centro de lnteligencia Artificial, Instituto Tecnol6gico y de Estudios Superiores de Monterrey, Monterrey, N.L., M~xico Abstract In recent years, attention has been devoted to abduction, a hypothetical form of non-monotonic reasoning that tries to fit the best 'explanation' to a given observation. In this paper we present a collection of applications of automated abductive reasoning developed in the Center for Artificial Intelligence of the ITESM (Monterrey, Mexico) in the last five years, covering a range from natural language understanding to software re-use. © 1998 Elsevier Science Ltd. All rights reserved 1. INTRODUCTION A usual common-sense reasoning problem consists in finding a 'good' explanation for an observed fact, given a base of knowledge and assumptions. For example, a physician tries to diagnose a disease that could produce the observed patients symptoms, based on his/her knowledge of diseases and their symptoms, the patients clinical history, and whatever she/he knows about the case. In abductive reasoning we try to find an explanation of a set of observed facts from a given knowledge base (Charniak, 1985). Abductive reasoning can be illustrated in the following way (Charniak, 1985): X causes Y Y is known to be true then X can be an explanation of Y. This kind of reasoning can be applied to a variety of tasks, including diagnosis and interpretation, which can be applied to many fields like medicine, machine ~: troubleshooting, natural language understanding and others; examples of all of the preceding will be presented in the following. A The structure of this paper is as follows: in Section 1.1 a precise description of the abductive reasoning frame- work is presented; in Section 2 we present an application of abductive reasoning to expert systems prototyping; in Section 3 we describe an application of automated abduction to software re-use, and finally in Section 4 we present an application of automated abduction to natural language understanding, followed by a general conclu- sion. 1.1. The Theorist Framework Theorist is a precise framework for abductive reasoning, as well as an implementation for automated abduction, proposed by David Poole (1994). It is based on the idea of 'theory formation'--that is, incremental formation of the 'theory' that explains the observed fact--from a fixed set of possible hypotheses. In other words, theory formation is seen as accepting as true instances of some of the hypotheses stored in the hypotheses set. Clearly the hypotheses set is composed of a set of declarations that could explain some of the observed facts. The user is responsible for providing an adequate set of plausible hypotheses. In Theorist, the user provides three sets of first order logic formulas: ~ is a set of closed formulas called facts. They are supposed to be true in the world that is being modeled. is a set of (possibly open) formulas that act as possible hypotheses, some of which could be used, once instantiated, as a part of an explanation, if it is consistent with the facts and the constraints. is a set of closed formulas taken as restrictions. * Email: rbrena@campus.mty.itesm.mx. ~This definition can be consulted in Poole (1988); they are included here just for the sake of completeness. 0957-4174/98/$19.00 Copyright © 1998 Elsevier Science Ltd. All rights reserved. PII S0957-4174(97)00066-3