Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/97847
Full metadata record
DC FieldValueLanguage
dc.contributor.authorWang, Wenwenen
dc.contributor.authorSubagdja, Budhitamaen
dc.contributor.authorTan, Ah-Hweeen
dc.contributor.authorTan, Yuan-Sinen
dc.date.accessioned2013-07-26T06:58:17Zen
dc.date.accessioned2019-12-06T19:47:19Z-
dc.date.available2013-07-26T06:58:17Zen
dc.date.available2019-12-06T19:47:19Z-
dc.date.copyright2012en
dc.date.issued2012en
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.identifier.urihttps://hdl.handle.net/10356/97847-
dc.identifier.urihttp://hdl.handle.net/10220/12402en
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.language.isoenen
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
dc.identifier.doi10.1109/IJCNN.2012.6252429en
item.fulltextNo Fulltext-
item.grantfulltextnone-
Appears in Collections:SCSE Conference Papers

Google ScholarTM

Check

Altmetric

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.