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.

Local reasoning and knowledge compilation for efficient temporal abduction

Abstract: Generating abductive explanations is the basis of several problem solving activities such as diagnosis, planning, and interpretation. Temporal abduction means generating explanations that do not only account for the presence of observations, but also for temporal information on them, based on temporal knowledge in the domain theory. We focus on the case where such a theory contains temporal constraints that are required to be consistent with temporal information on observations. Our aim is to propose efficient algorithms for computing temporal abductive explanations. Temporal constraints in the theory and in the observations can be used actively by an abductive reasoner in order to prune inconsistent candidate explanations at an early stage during their generation. However, checking temporal constraint satisfaction frequently generates some overhead. We analyze two incremental ways of making this process efficient. First we show how, using a specific class of temporal constraints (which is expressive enough for many applications), such an overhead can be reduced significantly, yet preserving a full pruning power. In general, the approach does not affect the asymptotic complexity of the problem, but it provides significant advantages in practical cases. We also show that, for some special classes of theories, the asymptotic complexity is also reduced. We then show how, compiled knowledge based on temporal information, can be used to further improve the computation, thus, extending to the temporal framework previous results in the case of atemporal abduction. The paper provides both analytic and experimental evaluations of the computational advantages provided by our approaches.

Active Inference and Abduction

Abstract: The background target of the research going into the present article is to forge an intellectual alliance between, on the one hand, active inference and the free-energy principle (FEP), and on the other, Charles S. Peirce’s theory of semiotics and pragmatism. In the present paper, the focus is on the allegiance between the nomenclatures of active and abductive inferences as the proper place to begin reaching at that wider target. The paper outlines the key conceptual elements involved in a naturalistic rendering of Peirce’s late semiotic and logical notion of abductive reasoning. The target is a cognitive-biological model of abduction which preserves the functional integrity of an organism and fulfils the existential imperative for living beings’ evidence of existence. Such a model is an adaptation of Peirce’s late logical schema of abduction proposed in his largely unpublished works during the early 20th century. The proposed model is argued to be a feasible sketch also of recent breakthroughs in computational (sensu Bayesian) cognitive science.

Learning from plausible explanations

Abstract: Publisher Summary This chapter explores the incomplete-theory problem in which a learning system has an explicit domain theory that cannot generate an explanation for every example. The general method is to use the existing domain theory to generate a plausible explanation of the example and to extract from it one or more rules that may then be added to the domain theory. This method is an application of abductive reasoning in that it is attempting to account for a known conclusion (the goal concept) by proposing various hypotheses, which, together with the existing domain theory, may account for it. If a complete explanation can be created for an example, the domain theory is adequate and need not be extended. If the example cannot be completely explained, there are usually many partial explanations that can be generated to explain it. The chapter presents the implementation of a prototype system that is able to extend its domain theory this way. The goal of this method is to increase the explanatory power of the domain theory rather than to acquire a specific way of recognizing instances of the goal concept.

Visual Explanation by High-Level Abduction: On Answer-Set Programming Driven Reasoning about Moving Objects

Abstract: We propose a hybrid architecture for systematically computing robust visual explanation(s) encompassing hypothesis formation, belief revision, and default reasoning with video data. The architecture consists of two tightly integrated synergistic components: (1) (functional) answer set programming based abductive reasoning with space-time tracklets as native entities; and (2) a visual processing pipeline for detection based object tracking and motion analysis. We present the formal framework, its general implementation as a (declarative) method in answer set programming, and an example application and evaluation based on two diverse video datasets: the MOTChallenge benchmark developed by the vision community, and a recently developed Movie Dataset.

A study on fuzzy abductive inference

Abstract: This paper proposes a fuzzy abductive inference method to realize a creative thinking support system. The fuzzy logic is applied to Peng and Reggia's (1990) abductive inference for handling degrees of manifestations. Application of the new method to a diagnostic problem is shown and the effectiveness of the proposed method is demonstrated by comparing the inferred results with those by the conventional fuzzy abduction. >