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
Citation
1
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Included in
Artificial Intelligence and Robotics Commons, Numerical Analysis and Scientific Computing Commons