A biologically-inspired cognitive agent model integrating declarative knowledge and reinforcement learning
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...
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| Main Authors: | , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2010
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| Subjects: | |
| 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 |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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. |
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