Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/97847
Title: A self-organizing multi-memory system for autonomous agents
Authors: Wang, Wenwen
Subagdja, Budhitama
Tan, Ah-Hwee
Tan, Yuan-Sin
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
http://hdl.handle.net/10220/12402
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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