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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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Main Authors: Chang Kee Tong, Chin Kim On, Teo, J., Mountstephens, J.
Format: Conference Proceeding
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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
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identifier ISSN: 1089-778X
ispartof 2012 IEEE Congress on Evolutionary Computation, 2012, p.1-8
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recordid cdi_ieee_primary_6256638
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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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