Abductive reasoning

About: Abductive reasoning is a research topic. Over the lifetime, 1917 publications have been published within this topic receiving 44645 citations. The topic is also known as: abduction & abductive inference.

Repairing missing is-a structure in ontologies is an abductive reasoning problem.

Abstract: With the increased use of ontologies in semantically-enabled applications, the issue of debugging defects in ontologies has become increasingly important. These defects can lead to wrong or incomplete results for the applications. Debugging consists of the phases of detection and repairing. In this paper we focus on the repairing phase of a particular kind of defects, i.e., the missing relations in the is-a hierarchy. We show that this can be formalized as an abduction problem. Further, we define properties for the ontology, the set of is-a relations to repair and the domain expert, as well as preference criteria on solutions and discuss the influences of these properties and criteria on the existence of solutions for the abduction problem. We also discuss the consequences of our analyses of the repairing problem for the development and use of debugging systems.

Abductive Inference in Bayesian Networks: A Review

Abstract: The goal of this paper is to serve as a survey for the problem of abductive inference (or belief revision) in Bayesian networks. Thus, the problem is introduced in its two variants: total abduction (or MPE) and partial abduction (or MAP) . Also, the problem is formulated in its general case, that is, looking for the K best explanations. Then, a (non exhaustive) review of exact and approximate algorithms for dealing with both abductive inference problems is carried out. Finally, we collect the main complexity results appeared in the literature for both problems (MPE and MAP).

AI approaches to abduction

Abstract: Abductive reasoning has gained increasing interest in many fields of AI research. Its utility was first observed for diagnostic tasks (cf. [Pople, 19731 or, e.g., [Console and Torasso, 1991; Console et al., 1991b]), but as many researchers have shown it is not limited to this use. Currently under investigation or suggested are such different applications as plan recognition (e.g., [Dragoni and Puliti, 1994; Helft and Konolige, 1990; Bauer and Paul, 1993; Bauer et al., 1993]), text understanding and generation (e.g., [Stickel, 1990]), program debugging (cf. [Charniak and McDermott, 1985]), test generation (see [Mcllraith, 1994]), planning (e.g., [Eshghi, 1991; Stone, 1998]), user modeling (cf. [Poole, 1988]), database updates (e.g., [Kakas and Mancarella, 1990a}), case-based reasoning (cf. [Leake, 1993; Satoh, 1998]), learning (cf. [Kakas et al., 1998; Lamma et al., 19971 or [Thompson and Mooney, 1990, temporal reasoning (e.g., [Li and Pereira, 19961), constraint handling (e.g., [Burckert and Nutt, 1992; Wetzel and Toni, 1998]) or vision (cf. [Charniak and McDermott, 1985]).

Using Abduction for Induction Based on Bottom Generalization

Abstract: Abduction is to find explanations which explain a given example assuming a background theory. Induction, often called inductive inference, means a process of generating general rules which given examples obey. From these simple definitions, we can expect such an inductive inference procedure that it generates rules by modifying explanations which some abductive inference generates from input examples. In this chapter we give such a procedure with the support of deductive inference and generalization. The procedure is a refinement of bottom generalization (Yamamoto, 1997; Yamamoto, 1999a), which was invented in the analysis of inverse entailment by (Muggleton, 1995). Because inverse entailment is an extension of bottom generalization, the results in this chapter show that inverse entailment also contains abduction potentially.