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Game AI generation using evolutionary multi-objective optimization
This paper presents the design and evaluation of a full AI controller for Real-Time Strategy (RTS) games using techniques from Evolutionary Computing (EC). The design is novel in its use of a modified Pareto Differential Evolution (PDE) algorithm for bi-objective optimization of the weights of an Ar...
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creator | Chang Kee Tong Chin Kim On Teo, J. Mountstephens, J. |
description | This paper presents the design and evaluation of a full AI controller for Real-Time Strategy (RTS) games using techniques from Evolutionary Computing (EC). The design is novel in its use of a modified Pareto Differential Evolution (PDE) algorithm for bi-objective optimization of the weights of an Artificial Neural Network (ANN) controller when only single-objective optimization has so far been studied. The two main aims of this research are to: (1) develop controllers capable of defeating opponents of varying difficulty levels, which may assist in commercial RTS AI development, and (2) minimize the number of neurons used in the ANN architecture, an issue primarily of efficiency. Experimental results using the popular Warcraft III platform demonstrate success with both aims: the optimized controller was able to win any battle using only a minimal number of hidden neurons, but sub-optimal controllers were able to provide opponents of any intermediate difficulty. |
doi_str_mv | 10.1109/CEC.2012.6256638 |
format | conference_proceeding |
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The design is novel in its use of a modified Pareto Differential Evolution (PDE) algorithm for bi-objective optimization of the weights of an Artificial Neural Network (ANN) controller when only single-objective optimization has so far been studied. The two main aims of this research are to: (1) develop controllers capable of defeating opponents of varying difficulty levels, which may assist in commercial RTS AI development, and (2) minimize the number of neurons used in the ANN architecture, an issue primarily of efficiency. 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Experimental results using the popular Warcraft III platform demonstrate success with both aims: the optimized controller was able to win any battle using only a minimal number of hidden neurons, but sub-optimal controllers were able to provide opponents of any intermediate difficulty.</description><subject>Artificial intelligence</subject><subject>Artificial Intelligence (AI)</subject><subject>Artificial neural networks</subject><subject>Artificial Neural Networks (ANN)</subject><subject>Evolutionary Multi-Objective Optimization (EMO)</subject><subject>Games</subject><subject>Humans</subject><subject>Neurons</subject><subject>Optimization</subject><subject>Pareto Differential Evolution (PDE)</subject><subject>Real-Time Strategy Game (RTS)</subject><subject>Vectors</subject><subject>Warcraft III</subject><issn>1089-778X</issn><issn>1941-0026</issn><isbn>1467315109</isbn><isbn>9781467315104</isbn><isbn>1467315087</isbn><isbn>9781467315081</isbn><isbn>1467315095</isbn><isbn>9781467315098</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo9kElLA0EQhdsNjDF3wUv_gR6ruqeXOcYhxkDAi4K3MJmpCR1mCbME9Nen1WBdHsX7qng8xh4QIkRIntJFGklAGRmpjVHugt1hbKxCDc5esgkmMQoAaa7-jXB2HQxwibDWfd6yWd_vIYx1iLGdsOdlVhOfr_iOGuqywbcNH3vf7Dgd22r82bPui9djNXjRbveUD_5IvD0Mvvbfv_w9uymzqqfZWafs42Xxnr6K9dtylc7XwqPVg9AuJ5KFzVURS6QMkhLQYeEKSKwhiwUqp5VWRGRNGZdb6RSCsmVITpSrKXv8--sDsTl0vg7JNucu1AkD1E6I</recordid><startdate>201206</startdate><enddate>201206</enddate><creator>Chang Kee Tong</creator><creator>Chin Kim On</creator><creator>Teo, J.</creator><creator>Mountstephens, J.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201206</creationdate><title>Game AI generation using evolutionary multi-objective optimization</title><author>Chang Kee Tong ; Chin Kim On ; Teo, J. ; Mountstephens, J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-58cee2d7c3d421ea09f0181d8d0976e71d1385353eee76f4fb2831037f778eec3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Artificial intelligence</topic><topic>Artificial Intelligence (AI)</topic><topic>Artificial neural networks</topic><topic>Artificial Neural Networks (ANN)</topic><topic>Evolutionary Multi-Objective Optimization (EMO)</topic><topic>Games</topic><topic>Humans</topic><topic>Neurons</topic><topic>Optimization</topic><topic>Pareto Differential Evolution (PDE)</topic><topic>Real-Time Strategy Game (RTS)</topic><topic>Vectors</topic><topic>Warcraft III</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chang Kee Tong</creatorcontrib><creatorcontrib>Chin Kim On</creatorcontrib><creatorcontrib>Teo, J.</creatorcontrib><creatorcontrib>Mountstephens, J.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Chang Kee Tong</au><au>Chin Kim On</au><au>Teo, J.</au><au>Mountstephens, J.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Game AI generation using evolutionary multi-objective optimization</atitle><btitle>2012 IEEE Congress on Evolutionary Computation</btitle><stitle>CEC</stitle><date>2012-06</date><risdate>2012</risdate><spage>1</spage><epage>8</epage><pages>1-8</pages><issn>1089-778X</issn><eissn>1941-0026</eissn><isbn>1467315109</isbn><isbn>9781467315104</isbn><eisbn>1467315087</eisbn><eisbn>9781467315081</eisbn><eisbn>1467315095</eisbn><eisbn>9781467315098</eisbn><abstract>This paper presents the design and evaluation of a full AI controller for Real-Time Strategy (RTS) games using techniques from Evolutionary Computing (EC). The design is novel in its use of a modified Pareto Differential Evolution (PDE) algorithm for bi-objective optimization of the weights of an Artificial Neural Network (ANN) controller when only single-objective optimization has so far been studied. The two main aims of this research are to: (1) develop controllers capable of defeating opponents of varying difficulty levels, which may assist in commercial RTS AI development, and (2) minimize the number of neurons used in the ANN architecture, an issue primarily of efficiency. Experimental results using the popular Warcraft III platform demonstrate success with both aims: the optimized controller was able to win any battle using only a minimal number of hidden neurons, but sub-optimal controllers were able to provide opponents of any intermediate difficulty.</abstract><pub>IEEE</pub><doi>10.1109/CEC.2012.6256638</doi><tpages>8</tpages></addata></record> |
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identifier | ISSN: 1089-778X |
ispartof | 2012 IEEE Congress on Evolutionary Computation, 2012, p.1-8 |
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language | eng |
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source | IEEE Xplore All Conference Series |
subjects | Artificial intelligence Artificial Intelligence (AI) Artificial neural networks Artificial Neural Networks (ANN) Evolutionary Multi-Objective Optimization (EMO) Games Humans Neurons Optimization Pareto Differential Evolution (PDE) Real-Time Strategy Game (RTS) Vectors Warcraft III |
title | Game AI generation using evolutionary multi-objective optimization |
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