Abstract

The notion of explanation is basilar in many human behaviors and indeed in many elds (such as philosophy or psychology) there is a long tradition in the study of such a notion. In particular, reasoning towards explanation is a basic task in many of the problem solving activities investigated by the AI community. For example, it is the core of diagnostic problem solving (whose goal is to explain symptoms observed in a system with the presence of some faults) or of interpretation activities (e.g., image or story interpretation or plan recognition), and it is a basic component of machine learning (where the goal is to produce some theory that accounts for and generalizes some phenomena). Following a logicist tradition, many AI researchers working on explanation reduced such a notion to deduction: a phenomenon is explained when it can be deduced from a domain theory, possibly after some assumptions. Indeed, the interesting case is the one where the observed phenomenon does not follow from the original theory and some assumptions have to be made; in particular, one may either assume the truth of some facts mentioned in the theory or extend the theory itself. The term \abduction has been often used to characterize the type of inference performed in the former case (while \induction is closer to the latter case); as a consequence, the basic pattern of abduction can be seen as the ability of assuming a fact A in case A is a possible explanation for another fact B to be explained. From a logical point of view, this corresponds to the unsound inference concluding A from A! B and B. Abduction is thus a form of non-classical inference whose properties (e.g., non-monotonicity) are similar to those of other non-classical logics proposed and studied in the AI literature. Given such premises, one would expect that abduction played a central role in the logical approaches to AI. In fact, however, many researchers are quite di dent about such a form of reasoning and some criticisms have arisen in the

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call