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.

Case-Based, Model-Based, and Explanation-Based Styles of Reasoning in Foreign Policy

Abstract: This article uses two types of experiments to examine the kinds of reasoning people use when thinking about foreign policy. Case-based, explanation-based, and model-based reasoning are offered as an appropriate taxonomy of reasoning styles, and laboratory experiments are the vehicle for empirical analysis. The first experiment uses a thought checking methodology. When combining over all subjects and scenarios in that experiment, explanation-based reasoning emerges as dominant, with the other two occurring with roughly equal probability. Case-based reasoning comes in second for general life scenarios and model-based reasoning comes in second for the international politics scenarios. The dominant role of explanation-based reasoning becomes even stronger for more expert respondents (graduate students in political science), and is not significantly diminished for respondents trained in the case method of instruction. The predominance of explanation-based thoughts over case-based and model-based thoughts is replicated, and even accentuated, in a second experiment involving a protocol analysis of unconstrained thoughts. The results of both types of experiments indicate that scholars concentrating on decision making would be remiss to represent reasoning processes as exclusively case or model or even explanation based. Reasoning in the area of foreign policy seems to be slightly more explanation based, but exhibits characteristics of each of the three modes of reasoning.

Detection of potential inconsistencies in knowledge bases

Abstract: We present a new computational approach to the problem of detection of potential inconsistencies in knowledge bases. For such inconsistencies, we characterize the sets of possible input facts that will allow the knowledge based system to derive the contradiction. the state-of-the-art approach to a solution of this problem is represented by the COVADIS system which checks simple rule bases. the COVADIS approach relies on forward chaining and is strongly related to the way ATMS computes labels for deducible facts. Here, we present an alternative computation method that employs backward chaining in a kind of abductive reasoning. This approach gives a more focused reasoning, thus requiring much less computation and memory than COVADIS. Further, since our method is very similar to SLD-resolution, it is suitable for handling the more powerful knowledge base form represented by Horn claause bases. Finally, our method is easily extended to uncertain knowledge bases, assuming that the uncertainty calculus is modeled by possibilistic logic. This extension allows us to model the effect of user defined belief thresholds for inference chains.

Inductive invariant generation via abductive inference

Abstract: This paper presents a new method for generating inductive loop invariants that are expressible as boolean combinations of linear integer constraints. The key idea underlying our technique is to per...

Human reasoning and argumentation: the probabilistic approach

Abstract: Book synopsis: This interdisciplinary work is a collection of major essays on reasoning: deductive, inductive, abductive, belief revision, defeasible (non-monotonic), cross cultural, conversational, and argumentative. They are each oriented toward contemporary empirical studies. The book focuses on foundational issues, including paradoxes, fallacies, and debates about the nature of rationality, the traditional modes of reasoning, as well as counterfactual and causal reasoning. It also includes chapters on the interface between reasoning and other forms of thought. In general, this last set of essays represents growth points in reasoning research, drawing connections to pragmatics, cross-cultural studies, emotion and evolution.

Simplifying explanations in Bayesian belief networks

Abstract: Abductive inference in Bayesian belief networks is intended as the process of generating the K most probable configurations given an observed evidence. These configurations are called explanations and in most of the approaches found in the literature, all the explanations have the same number of literals. In this paper we propose some criteria to simplify the explanations in such a way that the resulting configurations are still accounting for the observed facts. Computational methods to perform the simplification task are also presented. Finally the algorithms are experimentally tested using a set of experiments which involves three different Bayesian belief networks.