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

Stephen Richard Varey, Alessandro Di Stefano, The Anh Han

TL;DR

KERAIA addresses the persistent gap between tacit human expertise and executable AI in dynamic, context-rich environments by introducing Clouds of Knowledge, Dynamic Relationships, Lines of Thought, Cloud Elaboration, KSYNTH, and the General Purpose Paradigm Builder. The framework supports multi-paradigm reasoning and explainable AI within an integrated software platform that enables collaborative knowledge acquisition, dynamic knowledge transformation, and transparent execution. Evaluation across naval warfare, water-treatment diagnostics, and RISK gameplay demonstrates expressiveness, adaptability, and generalizability beyond static KR paradigms. The work also discusses implementation considerations and comparisons with ontologies, rule-based systems, and knowledge graphs, while outlining future directions in benchmarking, formal semantics, and integration with sub-symbolic AI.

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.

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

TL;DR

KERAIA addresses the persistent gap between tacit human expertise and executable AI in dynamic, context-rich environments by introducing Clouds of Knowledge, Dynamic Relationships, Lines of Thought, Cloud Elaboration, KSYNTH, and the General Purpose Paradigm Builder. The framework supports multi-paradigm reasoning and explainable AI within an integrated software platform that enables collaborative knowledge acquisition, dynamic knowledge transformation, and transparent execution. Evaluation across naval warfare, water-treatment diagnostics, and RISK gameplay demonstrates expressiveness, adaptability, and generalizability beyond static KR paradigms. The work also discusses implementation considerations and comparisons with ontologies, rule-based systems, and knowledge graphs, while outlining future directions in benchmarking, formal semantics, and integration with sub-symbolic AI.

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.
Paper Structure (25 sections, 4 figures, 1 table)

This paper contains 25 sections, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Foundational Knowledge Representation Paradigms
  4. Cognitive Models and Their Influence on AI Architectures
  5. Challenges and Advances in Knowledge Engineering
  6. The Ethics and Explainability in AI Systems
  7. Benchmarking in AI
  8. Limitations of Traditional Paradigms in Dynamic Contexts
  9. Architecture and Implementation
  10. Implementation Repository Overview
  11. Explainability (XAI)
  12. Lines of Thought (LoTs) in Detail
  13. Evaluation and Case Studies
  14. Naval Warfare Case Study Revisited
  15. KERAIA Implementation Details
  16. ...and 10 more sections

Figures (4)

  • Figure 1: KERAIA - Functional overview
  • Figure 2: Architecture
  • Figure 7: KERAIA Core Concepts.
  • Figure 8: Naval Scenario Evolution . Illustrates the dynamic flow of information and reasoning steps within the KERAIA framework during the naval threat detection scenario, involving sensor fusion (Cloud-SF), threat recognition (Cloud-TR), and fleet coordination (Cloud-FC) clouds, guided by Lines of Thought.