A Biologically-Inspired Cognitive Agent Model Integrating Declarative Knowledge and Reinforcement Learning

Abstract--The paper proposes a biologically-inspired cognitive agent model, known as FALCON-X, based on an integration of the Adaptive Control of Thought (ACT-R) architecture and a class of self-organizing neural networks called fusion Adaptive Resonance Theory (fusion ART). By replacing the production system of ACT-R by a fusion ART model, FALCONX integrates high-level deliberative cognitive behaviors and real-time learning abilities, based on biologically plausible neural pathways. We illustrate how FALCON-X, consisting of a core inference area interacting with the associated intentional, declarative, perceptual, motor and critic memory modules, can be used to build virtual robots for battles in a simulated RoboCode domain. The performance of FALCON-X demonstrates the efficacy of the hybrid approach. Keywords-Cognitive Agents; Knowledge Representation; Reinforcement Learning

• Type: Journal
• Year: 2010
• Where: IAT
• Authors: Ah-Hwee Tan, Gee Wah Ng
• Keywords: Deliberative Cognitive Behaviors, Fusion Adaptive Resonance, Fusion Art, IAT 2010, Intelligent Agents