Automated evaluation for AI controllers in tower defense game using genetic Algorithm

Rayner Alfred, and Tan , Tse Guan and Yong , Yung Nan and Chin , Kim On and Jason Teo, (2013) Automated evaluation for AI controllers in tower defense game using genetic Algorithm. Communications in Computer and Information Science, 378 . pp. 135-146. ISSN 1865-0929

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Official URL: Http://dx.doi.org/10.1007/978-3-642-40567-9_12

Abstract

"This paper presents the research result of implementing evolutionary algorithms towards computational intelligence in Tower Defense game (TD game). TD game is a game where player(s) need to build tower to prevent the creeps from reaching their based. Penalty will be given if player losses any creeps during gameplays. It is a suitable test bed for planning, designing, implementing and testing either new or modified AI techniques due to the complexity and dynamicity of the game. In this research, Genetic Algorithm (GA) will be implemented to the game with two different neural networks: (1) Feed- forward (FFNN) and (2) Elman Recurrent (ERNN) used as tuner of the weights. ANN will determine the placement of the towers and the fitness score will be calculated at the end of each game. As a result, it is proven that the implementation of GA towards FFNN is better compared to GA towards ERNN. "

Item Type:Article
Uncontrolled Keywords:Genetic Algorithm (GA) Artificial Neural-Network (ANN) Feedforward Neural Network (FFNN) Elman Recurrent Neural Network (ERNN) Tower Defense game (TD game) Strategy Games Artificial Intelligence (AI)
Subjects:Q Science > QA Mathematics > QA76 Computer software
Divisions:FACULTY > Faculty of Computing and Informatics
ID Code:12262
Deposited By:IR Admin
Deposited On:17 Nov 2015 12:04
Last Modified:17 Nov 2015 12:04

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