Revisiting Knowledge Representation Systems: A Critical Re-evaluation of Predicate Logic in AI Frameworks

Abstract

The integration of Artificial Intelligence (AI) into various sectors has generated a pressing need for sophisticated knowledge representation systems. This article investigates the limitations inherent in traditional predicate logic approaches and proposes a novel framework building on these findings. Through empirical analysis employing qualitative case studies and quantitative performance metrics, we demonstrate that current systems often fail to capture contextual nuances vital for real-world applications. A mixed-methods approach was utilized, encompassing an extensive literature review, expert interviews, and algorithmic performance assessment using a custom-built Python-based simulation environment. Our results indicate that existing predicate logic-based models exhibit error rates exceeding 15% in complex scenario responses, while our proposed framework achieves statistically significant performance improvements (p < 0.05). This research provides critical insights into how knowledge representation can evolve to better meet the demands of contemporary AI applications, thereby laying groundwork for future innovations in the field.