A biologically-inspired cognitive agent model integrating declarative knowledge and reinforcement learning

• Main Authors: TAN, Ah-hwee, NG, Gee-Wah
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2010
• Subjects: cognitive agents; knowledge representation; reinforcement learning; Artificial Intelligence and Robotics; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/6252; https://ink.library.smu.edu.sg/context/sis_research/article/7255/viewcontent/BICA_IAT_2010.pdf
• Institution: Singapore Management University

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, FALCON-X 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.