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dc.contributor.authorWang, Wenwenen
dc.contributor.authorSubagdja, Budhitamaen
dc.contributor.authorTan, Ah-Hweeen
dc.contributor.authorTan, Yuan-Sinen
dc.identifier.citationWang, 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).en
dc.description.abstractThis 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.en
dc.rights© 2012 IEEE.en
dc.subjectDRNTU::Engineering::Computer science and engineeringen
dc.titleA self-organizing multi-memory system for autonomous agentsen
dc.typeConference Paperen
dc.contributor.schoolSchool of Computer Engineeringen
dc.contributor.conferenceInternational Joint Conference on Neural Networks (2012 : Brisbane, Australia)en
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