An approach to abductive inference developed in the TACITUS project has resulted in a dramatic simplification of how the problem of interpreting texts is conceptualized. Its use in solving the local pragmatics problems of reference, compound nominals, syntactic ambiguity, and metonymy is described and illustrated. It also suggests an elegant and thorough integration of syntax, semantics, and pragmatics. 1

Abduction in Logic Programming started in the late 80s, early 90s, in an attempt to extend logic programming into a framework suitable for a variety of problems in Artificial Intelligence and other areas of Computer Science. This paper aims to chart out the main developments of the field over the last ten years and to take a critical view of these developments from several perspectives: logical, epistemological, computational and suitability to application. The paper attempts to expose some of the challenges and prospects for the further development of the field.

Representing and reasoning with priorities are important in commonsense reasoning. This paper introduces a framework of prioritized logic programming (PLP), which has a mechanism of explicit representation of priority information in a program. When a program contains incomplete or indefinite information, PLP is useful for specifying preference to reduce non-determinism in logic programming. Moreover, PLP can realize various forms of commonsense reasoning in AI such as abduction, default reasoning, circumscription, and their prioritized variants. The proposed framework increases the expressive power of logic programming and exploits new applications in knowledge representation. Keywords: prioritized logic programs, abduction, default reasoning, prioritized circumscription 1 Introduction In commonsense reasoning a theory is usually assumed incomplete and may contain indefinite or conflicting knowledge. Under such circumstances, priority information is useful to select appropriate know...

Abduction is a form of non-monotonic reasoning that has gained increasing interest in the last few years. The key idea behind it can be represented by the following inference rule $$O = \mathop C\limits_| - N = \mathop P\limits_|^| - O - \mathop C\limits_|^| - .$$ i.e., from an occurrence of ohgr and the rule ldquophiv implies ohgrrdquo, infer an occurrence of phiv as aplausible hypothesis or explanation for ohgr. Thus, in contrast to deduction, abduction is as well as induction a form of ldquodefeasiblerdquo inference, i.e., the formulae sanctioned are plausible and submitted to verification. In this paper, a formal description of current approaches is given. The underlying reasoning process is treated independently and divided into two parts. This includes a description of methods for hypotheses generation and methods for finding the best explanations among a set of possible ones. Furthermore, the complexity of the abductive task is surveyed in connection with its relationship to default reasoning. We conclude with the presentation of applications of the discussed approaches focusing on plan recognition and plan generation.

Abduction is often viewed as inference to the "best" explanation. However, the evaluation of the goodness of candidate hypotheses remains an open problem. Most artificial intelligence research addressing this problem has concentrated on syntactic criteria, applied uniformly regardless of the explainer's intended use for the explanation. We demonstrate that syntactic approaches are insufficient to capture important differences in explanations, and propose instead that choice of the "best" explanation should be based on explanations' utility for the explainer 's purpose. We describe two classes of goals motivating explanation: knowledge goals reflecting internal desires for information, and goals to accomplish tasks in the external world. We describe how these goals impose requirements on explanations, and discuss how we apply those requirements to evaluate hypotheses in two computer story understanding systems. In order to learn from experience, a reasoner must be able to explain what...

In the doctoral dissertation from which this chapter is drawn, Ashwin Ram presented an alternative perspective on the processes of story understanding, explanation, and learning. The issues that Ram explores in that dissertation are similar to those that are explored by the other authors in this book, but the angle that Ram takes on these issues is somewhat different. Ram's exploration of

In this work, the problem of performing abduction in modal logics is addressed, along the lines of [3], where a proof theoretical abduction method for full first order classical logic is defined, based on tableaux and Gentzen-type systems. This work applies the same methodology to face modal abduction. The non-classical context enforces the value of analytical proof systems as tools to face the meta-logical and proof-theoretical questions involved in abductive reasoning. The similarities and differences between quantifiers and modal operators are investigated and proof theoretical abduction methods for the modal systems K, D, T and S4 are defined, that are sound and complete. The construction of the abductive explanations is in strict relation with the expansion rules for the modal logics, in a modular manner that makes local modifications possible. The method given in this paper is general, in the sense that it can be adapted to any propositional modal logic for which analytic tableau...

Since linear resolution with clause ordering is incomplete for consequence-finding, it has been used mainly for proof-finding. In this paper, we re-evaluate consequence-finding. Firstly, consequence-finding is generalized to the problem in which only interesting clauses having a certain property (called characteristic clauses) should be found. Then, we show how adding a skip rule to ordered linear resolution makes it complete for consequence-finding in this general sense. Compared with set-of-support resolution, the proposed method generates fewer clauses to find such a subset of consequences. In the propositional case, this is an elegant tool for computing the prime implicants/implicates. The importance of the results lies in their applicability to a wide class of AI problems including procedures for nonmonotonic and abductive reasoning and truth maintenance systems. 1

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We investigate two realizations of parallel abductive reasoning systems using the model generation theorem prover MGTP. The first one, called the MGTP+MGTP method, is a co-operative problem-solving architecture in which model generation and consistency checking communicate with each other. There, parallelism is exploited by checking consistencies in parallel. However, since this system consists of two different components, the possibilities for parallelization are limited. In contrast, the other method, called the Skip method, does not separate the inference engine from consistency checking, but realizes both functions in only one MGTP that is used as a generateand-test mechanism. In this method, multiple models can be kept in distributed memories, thus a great amount of parallelism can be obtained. We also attempt the upside-down metainterpretation approach for abduction, in which top-down reasoning is simulated by a bottomup reasoner. 1