HEURISTICS FOR A DEFAULT LOGIC REASONING SYSTEM

Abstract

In Artificial Intelligence, Default Logic is recognized as a powerful framework for knowledge representation when one has to deal with incomplete information. Its expressive power is suitable for non monotonic reasoning, but the counterpart is its very high level of theoretical complexity. Today, some operational systems are able to deal with real world applications. However, finding a default logic extension in a practical way is not yet possible in whole generality. This paper which is an extended version of18 shows how heuristics such as Genetic Algorithms and Local Search techniques can be used and combined to build an automated default reasoning system. We give a general description of the required basic components and we exhibit experimental results.