KERAIA: An Adaptive and Explainable Framework for Dynamic Knowledge Representation and Reasoning

TL;DR

KERAIA is an innovative, adaptable, and explainable framework for dynamic knowledge representation and reasoning, integrating multiple inference methods and supporting complex, context-sensitive environments with practical case studies.

Contribution

This work introduces KERAIA, a novel framework combining dynamic aggregation, context-sensitive inheritance, and traceable reasoning, advancing symbolic knowledge engineering for complex AI applications.

Findings

Validated through case studies in naval warfare, industrial diagnostics, and strategic games.

Demonstrated superior expressiveness and adaptability over traditional paradigms.

Showcased effective integration of diverse inference methods within a unified platform.

Abstract

In this paper, we introduce KERAIA, a novel framework and software platform for symbolic knowledge engineering designed to address the persistent challenges of representing, reasoning with, and executing knowledge in dynamic, complex, and context-sensitive environments. The central research question that motivates this work is: How can unstructured, often tacit, human expertise be effectively transformed into computationally tractable algorithms that AI systems can efficiently utilise? KERAIA seeks to bridge this gap by building on foundational concepts such as Minsky's frame-based reasoning and K-lines, while introducing significant innovations. These include Clouds of Knowledge for dynamic aggregation, Dynamic Relations (DRels) for context-sensitive inheritance, explicit Lines of Thought (LoTs) for traceable reasoning, and Cloud Elaboration for adaptive knowledge transformation. This approach moves beyond the limitations of traditional, often static, knowledge representation paradigms. KERAIA is designed with Explainable AI (XAI) as a core principle, ensuring transparency and interpretability, particularly through the use of LoTs. The paper details the framework's architecture, the KSYNTH representation language, and the General Purpose Paradigm Builder (GPPB) to integrate diverse inference methods within a unified structure. We validate KERAIA's versatility, expressiveness, and practical applicability through detailed analysis of multiple case studies spanning naval warfare simulation, industrial diagnostics in water treatment plants, and strategic decision-making in the game of RISK. Furthermore, we provide a comparative analysis against established knowledge representation paradigms (including ontologies, rule-based systems, and knowledge graphs) and discuss the implementation aspects and computational considerations of the KERAIA platform.