Struggling with incomplete information in AI? This paper explores the use of heuristics, specifically Genetic Algorithms and Local Search techniques, to create an automated default reasoning system for knowledge representation. Recognizing Default Logic as a powerful framework for dealing with incomplete information, the study addresses its theoretical complexity by proposing practical solutions. The research details the basic components required for building such a system, focusing on methods to find default logic extensions in a practical and efficient manner. The integration of Genetic Algorithms and Local Search offers a novel approach to tackling the computational challenges inherent in default reasoning. Experimental results demonstrate the potential of these heuristics in constructing an automated default reasoning system, providing a valuable tool for real-world AI applications. This work contributes to the development of more robust and efficient knowledge representation and reasoning systems.
Published in the International Journal on Artificial Intelligence Tools, this research aligns perfectly with the journal's focus on practical tools and techniques in AI. By exploring heuristics for default logic reasoning, the paper addresses a core area of AI, offering solutions directly applicable to real-world systems.