Automated Knowledge Acquisition by Relevant Reasoning Based on Strong Relevant Logic

Abstract

AbstractAlmost all existing methodologies and automated tools for knowledge acquisition are somehow based on classical mathematical logic or its various classical conservative extensions. This paper proposes a new approach to knowledge acquisition problem: automated knowledge acquisition by relevant reasoning based on strong relevant logic. The paper points out why any of the classical mathematical logic, its various classical conservative extensions, and traditional relevant logics is not a suitable logical basis for knowledge acquisition, shows that strong relevant logic is a more hopeful candidate for the purpose, and establishes a conceptional foundation for automated knowledge acquisition by relevant reasoning based on strong relevant logic.KeywordsKnowledge AcquisitionRelevant LogicConservative ExtensionObject LogicMaterial ImplicationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.