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

2025-05-07

AI Summary

This paper addresses the challenge of transforming tacit human expertise into computable, executable, and interpretable knowledge. To this end, it proposes a knowledge representation and reasoning framework tailored for dynamic, complex scenarios. Methodologically, it introduces the novel “Knowledge Cloud” model, integrating dynamic relational modeling, explicit reasoning-path characterization, and cloud-refinement mechanisms—grounded in Minsky’s frame theory, the KSYNTH knowledge description language, a General Paradigm Pattern Builder (GPPB), and eXplainable AI (XAI) design principles. Unlike static ontologies, rule-based systems, or conventional knowledge graphs, the framework enables adaptive knowledge evolution and traceable, auditable inference. Empirical validation across three distinct domains—naval combat simulation, water treatment fault diagnosis, and RISK strategic decision-making—demonstrates substantial improvements in knowledge expressivity, task adaptability, and decision interpretability.

Technology Category

Knowledge Representation and Reasoning: Knowledge AcquisitionData Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, TrustCognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsUser Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalizationGraph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Transforming human expertise into computational algorithms
• Dynamic knowledge representation in complex environments
• Ensuring explainable and adaptive reasoning processes

Innovation

• Dynamic aggregation using Clouds of Knowledge
• Context-sensitive inheritance via Dynamic Relations
• Traceable reasoning with Lines of Thought