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

arXiv: https://arxiv.org/abs/2505.04313 · HTML: https://mlsanity.com/paper/2505.04313/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 54 references that are in the map.
- First posted on arXiv 7 May 2025.

## Builds on (most cited first, 9 of 9)

- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Metrics for Explainable AI: Challenges and Prospects](https://mlsanity.com/paper/1812.04608/) (2018)
- [The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence](https://mlsanity.com/paper/2002.06177/) (2020)
- [ChatGPT: More than a Weapon of Mass Deception, Ethical challenges and responses from the Human-Centered Artificial Intelligence (HCAI) perspective](https://mlsanity.com/paper/2304.11215/) (2023)
- [Using Digital Twins and Intelligent Cognitive Agencies to Build Platforms for Automated CxO Future of Work](https://mlsanity.com/paper/1808.07627/) (2018)
- [Reasoning with Contextual Knowledge and Influence Diagrams](https://mlsanity.com/paper/2007.00571/) (2020)

## Built on by (most cited first, 1 of 1)

- [Implementing Knowledge Representation and Reasoning with Object Oriented Design](https://mlsanity.com/paper/2601.14840/) (2026)

## Abstract (arXiv)

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.