A self-organizing multi-memory system for autonomous agents
This paper presents a self-organizing approach to the learning of procedural and declarative knowledge in parallel using independent but interconnected memory models. The proposed system, employing fusion Adaptive Resonance Theory (fusion ART) network as a building block, consists of a declarative m...
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| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/97847 http://hdl.handle.net/10220/12402 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | This paper presents a self-organizing approach to the learning of procedural and declarative knowledge in parallel using independent but interconnected memory models. The proposed system, employing fusion Adaptive Resonance Theory (fusion ART) network as a building block, consists of a declarative memory module, that learns both episodic traces and semantic knowledge in real time, as well as a procedural memory module that learns reactive responses to its environment through reinforcement learning. More importantly, the proposed multi-memory system demonstrates how the various memory modules transfer knowledge and cooperate with each other for a higher overall performance. We present experimental studies, wherein the proposed system is tasked to learn the procedural and declarative knowledge for an autonomous agent playing in a first person game environment called Unreal Tournament. Our experimental results show that the multi-memory system is able to enhance the performance of the agent in a real time environment by utilizing both its procedural and declarative knowledge. |
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