Genetic Algorithms for Extension Search in Default Logic

TL;DR

This paper explores the application of Genetic Algorithms to efficiently search for extensions in Default Logic, addressing the computational complexity of the problem with experimental validation.

Contribution

It introduces a novel approach combining Genetic Algorithms with Default Logic extension search, demonstrating its potential effectiveness.

Findings

Genetic Algorithms can effectively find extensions in complex default theories.

The proposed method shows promising experimental results.

The approach offers a new heuristic for default reasoning tasks.

Abstract

A default theory can be characterized by its sets of plausible conclusions, called its extensions. But, due to the theoretical complexity of Default Logic (Sigma_2p-complete), the problem of finding such an extension is very difficult if one wants to deal with non trivial knowledge bases. Based on the principle of natural selection, Genetic Algorithms have been quite successfully applied to combinatorial problems and seem useful for problems with huge search spaces and when no tractable algorithm is available. The purpose of this paper is to show that techniques issued from Genetic Algorithms can be used in order to build an efficient default reasoning system. After providing a formal description of the components required for an extension search based on Genetic Algorithms principles, we exhibit some experimental results.