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.

Large-Scale cost-based abduction in full-fledged first-order predicate logic with cutting plane inference

Abstract: Abduction is inference to the best explanation. Abduction has long been studied intensively in a wide range of contexts, from artificial intelligence research to cognitive science. While recent advances in large-scale knowledge acquisition warrant applying abduction with large knowledge bases to real-life problems, as of yet no existing approach to abduction has achieved both the efficiency and formal expressiveness necessary to be a practical solution for large-scale reasoning on real-life problems. The contributions of our work are the following: (i) we reformulate abduction as an Integer Linear Programming (ILP) optimization problem, providing full support for first-order predicate logic (FOPL); (ii) we employ Cutting Plane Inference, which is an iterative optimization strategy developed in Operations Research for making abductive reasoning in full-fledged FOPL tractable, showing its efficiency on a real-life dataset; (iii) the abductive inference engine presented in this paper is made publicly available.

Generation and Selection of Abductive Explanations for Non-Omniscient Agents

Abstract: Among the non-monotonic reasoning processes, abduction is one of the most important. Usually described as the process of looking for explanations, it has been recognized as one of the most commonly used in our daily activities. Still, the traditional definitions of an abductive problem and an abductive solution mention only theories and formulas, leaving agency out of the picture. Our work proposes a study of abductive reasoning from an epistemic and dynamic perspective. In the first part we explore syntactic definitions of both an abductive problem in terms of an agent's information and an abductive solution in terms of the actions that modify the agent's information. We look at diverse kinds of agents, including not only omniscient ones but also those whose information is not closed under logical consequence and those whose reasoning abilities are not complete. In the second part, we look at an existing logical framework whose semantic model allows us to interpret the previously stated formulas, and we define two actions that represent forms of abductive reasoning.

Weight learning in a probabilistic extension of answer set programs

Abstract: LPMLN is a probabilistic extension of answer set programs with the weight scheme derived from that of Markov Logic. Previous work has shown how inference in LPMLN can be achieved. In this paper, we present the concept of weight learning in LPMLN and learning algorithms for LPMLN derived from those for Markov Logic. We also present a prototype implementation that uses answer set solvers for learning as well as some example domains that illustrate distinct features of LPMLN learning. Learning in LPMLN is in accordance with the stable model semantics, thereby it learns parameters for probabilistic extensions of knowledge-rich domains where answer set programming has shown to be useful but limited to the deterministic case, such as reachability analysis and reasoning about actions in dynamic domains. We also apply the method to learn the parameters for probabilistic abductive reasoning about actions.

Find Out A New Method to Study Abductive Reasoning in Empirical Research

Abstract: This article presents a new tool that provides a methodological context to observe and analyze, both qualitatively and quantitatively, manifestations of abductive reasoning in empirical research. A...

Data Integration Using ID-Logic

Abstract: ID-Logic is a knowledge representation language that extends first-order logic with non-monotone inductive definitions. This paper introduces an ID-Logic based framework for database schema integration. It allows us to to uniformly represent and reason with independent source databases that contain information about a common domain, but may have different schemas. The ID-Logic theories that are obtained are called mediator-based systems. We show that these theories properly capture the common methods for data integration (i.e., global-as view and local-as-view with either exact or partial definitions), and apply on them a robust abductive inference technique for query answering.