A study on the logic of abduction

This paper introduces various inference rules (deduction, abduction, and induction) in Non-Axiomatic Logic. These rules are represented in a term-oriented language, and justified according to a common semantic foundation. The implementation and application of these rules are briefly described. Finally, this approach is compared with the other approaches, specially with respect to abduction.

Logic Journal of IGPL, 1996

Abduction is a topic that attracts much interest in AI and automated reasoning research. Di erent approaches have been devised, that give a formalized account of explanatory reasoning, propose methods to compute explanations, frame abduction in the context of logic programming. However, the logical nature of abduction is still far from being clear and di erent speci cations of the key underlying concepts have been given, that make it di cult to speak of abduction as a single wellde ned form of reasoning. This work is a preliminary discussion on the logical nature of abductive reasoning, emphasizing the fundamental di erence between abductive and deductive inference. Some logical properties of the inference to the \best explanation" are put forward and analyzed when the underlying logic is any extension of classical propositional logic (rst order logic, modal logic) or a non monotonic system.

Theoretical Computer Science, 1997

Abduction{ from observations and a theory, nd using hypotheses an explanation for the observations { gained increasing interest during the last years. This form of reasoning has wide applicability in di erent areas of computer science; in particular, it has been recognized as an important principle of common-sense reasoning.

This paper is a survey and critical overview of recent work on the extension of Logic Programming to perform Abductive Reasoning (Abductive Logic Programming). It updates the earlier paper \Abductive Logic Programming" 88]. We outline the general framework of Abduction and its applications to Knowledge Assimilation and Default Reasoning; we describe the argumentation-theoretic approach to the use of abduction as an interpretation for Negation as Failure, introduced in the earlier version 88] of this paper; and we present recent work on the generalisation of the argumentation-theoretic approach to provide a framework for default reasoning in general. We also analyse the links between Abduction and Constraint Logic Programming, as well as between Abduction and the extension of Logic Programming obtained by adding a form of explicit negation. Finally we discuss the relation between Abduction and Truth Maintenance.

Handbook of Logic in Artificial Intelligence and Logic Programming: Volume 5: Logic Programming, 1998

This paper extends and updates our earlier survey and analysis of work on the extension of logic programming to perform abductive reasoning [Kakas et al., 1993]. The purpose of the paper is to provide a critical overview of some of the main research results, in order to develop a common framework for evaluating these results, to identify the main unresolved problems, and to indicate directions for future work. The emphasis is not on technical details but on relationships and common features of different approaches. Some of the main issues we will consider are the contributions that abduction can make to the problems of reasoning with incomplete or negative information, the evolution of knowledge, and the semantics of logic programming and its extensions. We also discuss recent work on the argumentation-theoretic interpretation of abduction, which was introduced in the earlier version of this paper. The philosopher Peirce first introduced the notion of abduction. In [Peirce, 1931-58]...

Theoria an International Journal For Theory History and Foundations of Science, 2007

Atocha Aliseda's Abductive Reasoning (2006) gives a structural characterization of the "forward" explanatory reasoning from a theory to observational data. This paper asks whether there are any interesting structural rules for the "backward" abductive reasoning from observations to explanatory theories. Ignoring statistical cases, a partial explication of abduction is converse deductive explanation: h is abducible from e iff h deductively explains e. This relation of abducibility trivially satisfies Converse Entailment (if h entails e, then h is abducible from e), but it does not generally satisfy Converse Consequence (if h is abducible from e and g entails h, then g is abducible from e), since deductive explanation is not always transitive.

Journal of Logic, Language and Information, 2005

Abduction was first introduced in the epistemological context of scientific discovery. It was more recently analyzed in artificial intelligence, especially with respect to diagnosis analysis or ordinary reasoning. These two fields share a common view of abduction as a general process of hypotheses formation. More precisely, abduction is conceived as a kind of reverse explanation where a hypothesis H can be abduced from events E if H is a “good explanation” of E. The paper surveys four known schemes for abduction that can be used in both fields. Its first contribution is a taxonomy of these schemes according to a common semantic framework based on belief revision. Its second contribution is to produce, for each non-trivial scheme, a representation theorem linking its semantic framework to a set of postulates. Its third contribution is to present semantic and axiomatic arguments in favor of one of these schemes, “ordered abduction,” which has never been vindicated in the literature.

Expert Systems with Applications, 1998

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.

Artificial intelligence, 1995

We propose a model of abduction based on the revision of the epistemic state of an agent. Explanations must be sufficient to induce belief in the sentence to be explained (for instance, some observation), or ensure its consistency with other beliefs, in a manner that adequately accounts for factual and hypothetical sentences. Our model will generate explanations that nonmonotonically predict an observation, thus generalizing most current accounts, which require some deductive relationship between explanation and observation. It also provides a natural preference ordering on explanations, defined in terms of normality or plausibility. To illustrate the generality of our approach, we reconstruct two of the key paradigms for model-based diagnosis, abductive and consistency-based diagnosis, within our framework. This reconstruction provides an alternative semantics for both and extends these systems to accommodate our predictive explanations and semantic preferences on explanations. It also illustrates how more general information can be incorporated in a principled manner.