Evolution strategies for evolving artificial neural networks in an arcade game

Tse, Guan Tan and Teo, Jason Tze Wi and Patricia Anthony (2010) Evolution strategies for evolving artificial neural networks in an arcade game. In: 5th International Conference on Knowledge Management, 25-27 May 2010, Kuala Terengganu, Malaysia.

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The aim of this paper is to use a simple but powerful evolutionary algorithm called Evolution Strategies (ES) to evolve the connection weights and biases of feed-forward artificial neural networks (ANN) and to examine its learning ability through computational experiments in a non-deterministic and dynamic environment, which is the well-known arcade game called Ms. Pac-man. The resulting algorithm is referred to as an Evolution Strategies Neural Network or ESNet. This study is an attempt to create an autonomous intelligent controller to play the game. The comparison of ESNet with two random systems, Random Direction (RandDir) and Random Neural Network (RandNet) yields promising results.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Uncontrolled Keywords: Evolution Strategies, Evolutionary Artificial Neural Networks, Ms. Pac-man
Subjects: Q Science > QA Mathematics > QA1-939 Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
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Divisions: SCHOOL > School of Engineering and Information Technology
Depositing User: ADMIN ADMIN
Date Deposited: 14 Mar 2011 11:35
Last Modified: 06 Feb 2015 09:44
URI: https://eprints.ums.edu.my/id/eprint/2266

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