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