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.

A Sequential Model for Reasoning about Bargaining in Logic Programs

Abstract: This paper presents a sequential model of bargaining based on abductive reasoning in ASP We assume that each agent is represented by a logic program that encodes the background knowledge of the agent Each agent has a set of goals to achieve but these goals are normally unachievable without an agreement from the other agent We design an alternating-offers procedure that shows how an agreement between two agents can be reached through a reasoning process based on answer set programming and abduction We prove that the procedure converges to a Nash equilibrium if each player makes rational offer/counter-offer at each round

Knowledge Representation and Reasoning Under Uncertainty: Logic at Work

Abstract: The role of expectations in reasoning.- On logics of approximate reasoning.- Gentzen sequent calculus for possibilistic reasoning.- A model of inductive reasoning.- Automated reasoning with uncertainties.- An axiomatic approach to systems of prior distributions in inexact reasoning.- Contradiction removal semantics with explicit negation.- Logic programming for non-monotonic reasoning.- Agent oriented programming: An overview of the framework and summary of recent research.- An application of temporal logic for representation and reasoning about design.- Knowledge theoretic properties of topological spaces.- Rough logic for multi-agent systems.- A logical approach to multi-sources reasoning.- Situation theory and social structure.

Explanatory Inference under Uncertainty

Abstract: This paper investigates the performance of explanatory or abductive inference in certain hypothesis selection tasks. The strategy is to use various measures of explanatory power to compare competing hypotheses and then make an inference to the best explanation. Computer simulations are used to compare the accuracy of such approaches with a standard approach when uncertainty is present and when several causal scenarios occur including one where the conditions for explaining away are met. Results show that some explanatory approaches can perform well and in certain scenarios they perform much better than the standard approach.

Intelligent Information Integration as a Constraint Handling Problem

Abstract: Intelligent Information Integration (I3) aims at combining heterogeneous and distributed information sources by explicitly representing and reasoning about their content, giving the user the illusion of interacting with a uniform system. In this paper we show how query planning in such a system can be reduced to a constraint handling problem. Conceptually, our approach relies on a generalized abductive reasoning mechanism involving so called partially open predicates, which support a seamless combination of backward (goal-directed) and forward reasoning. The original aspects of our approach consist in the early detection of (plan) inconsistencies using forward propagation of constraints as well as in the seamless interleaving of query planning and execution. Unlike other specialized query planning algorithms, for which domain reasoning and query planning are only loosely coupled, our encoding of source and domain models into Constraint Handling Rules allows us to fully and efficiently exploit existing domain knowledge. The ability to automatically derive source interactions from domain models (ontologies) enhances the flexibility of modeling.