Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

9-2010

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, 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.

Keywords

cognitive agents, knowledge representation, reinforcement learning

Discipline

Artificial Intelligence and Robotics | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering; Intelligent Systems and Optimization

Publication

Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2010)

Volume

2

ISBN

9781424484829

Identifier

10.1109/WI-IAT.2010.210

Publisher

IEEE

City or Country

Toronto, Canada

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