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|Title:||A self-organizing multi-memory system for autonomous agents||Authors:||Wang, Wenwen
|Keywords:||DRNTU::Engineering::Computer science and engineering||Issue Date:||2012||Source:||Wang, W., Subagdja, B., Tan, A.-H., & Tan, Y-S. (2012). A self-organizing multi-memory system for autonomous agents. The 2012 International Joint Conference on Neural Networks (IJCNN).||Abstract:||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.||URI:||https://hdl.handle.net/10356/97847
|DOI:||10.1109/IJCNN.2012.6252429||Rights:||© 2012 IEEE.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||SCSE Conference Papers|
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