Tan, Kar Bin and Teo, Jason Tze Wi and Patricia Anthony, (2011) A multi-objective neuro-evolutionary optimization approach to intelligent game AI synthesis. In: 2011 International Conference on Information Technology and Multimedia: "Ubiquitous ICT for Sustainable and Green Living", ICIM 2011, 14-16 November 2011, Kajang, Selangor, Malaysia.
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Official URL: http://dx.doi.org/10.1109/ICIMU.2011.6122716
Numerous traditional board games such as Backgammon, Chess, Tic-Tac-Toc, Othello, Checkers, and Go have been used as research test-beds for assessing the performance of myriad computational intelligence systems including evolutionary algorithms (EAs) and artificial neural networks (ANNs). Approaches included building intelligent search algorithms to find the required solutions in such board games by searching through the solutions space stochastically. Recently, one particular type of search algorithm has been receiving a lot of interest in solving such kinds of game problems, which is the multi-objective evolutionary algorithms (MOEAs). Unlike single-objective optimization based search algorithms, MOEAs are able to find a set of non-dominated solutions which trades-off among all the conflicting objectives. In this study, the utilization of a multi-objective approach in evolving ANNs for Go game is investigated. A simple three layered feed-forward ANN is used and evolved with Pareto Archived Evolution Strategies (PAES) for computer players to learn and play the small board Go games.
|Item Type:||Conference Paper (UNSPECIFIED)|
|Uncontrolled Keywords:||Artificial neural networks, Computer go, Multi-objective evolutionary algorithms, Multi-objective optimization, Pareto archived evolution strategies|
|Subjects:||Q Science > QA Mathematics|
Q Science > QA Mathematics > QA76 Computer software
|Divisions:||SCHOOL > School of Engineering and Information Technology|
|Deposited By:||IR Admin|
|Deposited On:||12 Jul 2012 12:39|
|Last Modified:||30 Dec 2014 09:42|
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