Proceedings

CogTwin: A Hybrid Cognitive Architecture Framework for Adaptable and Cognitive Digital Twins

Sukanya Mandal, Noel E. O'Connor

Download BibTeX Cite

Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. AI4Tech: AI Enabling Technologies. Pages 9286-9295.

https://doi.org/10.24963/ijcai.2025/1032

Keywords

  • Advanced AI4Tech: Multimodal AI4Tech
  • Advanced AI4Tech: Metaverse AI4Tech
  • Emerging AI4Tech: Human-like emerging AI4Tech
  • Domain-specific AI4Tech: AI4Home and AI4City

Abstract

Current Digital Twin (DT) technology lacks the cognitive capabilities needed for true autonomy and intelligent adaptation. This paper introduces CogTwin, a hybrid cognitive architecture framework for developing Cognitive Digital Twins (CDTs). CogTwin integrates a 50ms cognitive cycle inspired by human cognition, dual knowledge graphs (static Domain Knowledge Repository (DKR) and dynamic Internal Knowledge Graph (DIKG)), a hybrid attention mechanism, and self-healing capabilities. Combining symbolic, sub-symbolic, and neuro-symbolic AI, CogTwin enables real-time learning and decision-making. Simulated smart city scenarios, including traffic incident management and power outage response, demonstrate CogTwin’s potential. Preliminary performance evaluations of the pseudocode suggest feasibility of the target 50ms cycle. The architecture also incorporates explainable AI (XAI) for transparency and human-CogTwin collaboration. CogTwin contributes towards a unified theory of cognition for DTs, laying the groundwork for more sophisticated and autonomous CDTs.

Sukanya Mandal, and Noel E. O'Connor. 2025. CogTwin: A Hybrid Cognitive Architecture Framework for Adaptable and Cognitive Digital Twins. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 9286–9295. https://doi.org/10.24963/ijcai.2025/1032
Welcome Future Conferences AI Journal Division IJCAI Proceedings About IJCAI IJCAI Awards