Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
The Equivalence of Support Vector Machine and Regularization Neural Networks
Neural Processing Letters
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Challenge-Sensitive Action Selection: an Application to Game Balancing
IAT '05 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Using coevolution to understand and validate game balance in continuous games
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Multi-objective rule mining using a chaotic particle swarm optimization algorithm
Knowledge-Based Systems
A support vector machine-based model for detecting top management fraud
Knowledge-Based Systems
A hybrid particle swarm optimization approach for clustering and classification of datasets
Knowledge-Based Systems
Multi-objective hybrid evolutionary algorithms for radial basis function neural network design
Knowledge-Based Systems
Learning to play games using a PSO-based competitive learning approach
IEEE Transactions on Evolutionary Computation
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Game balancing affects the gaming experience of players in video-games. In this paper, we propose a novel system, team ability balancing system (TABS), which is developed for automatically evaluating the performance of two teams in a role-playing video game. TABS can be used for assisting game designers to improve team balance. In TABS, artificial neural network (ANN) controllers learn to play the game in an unsupervised manner and they are evolved by using particle swarm optimization. The ANN controllers control characters of the two teams to fight with each other. An evaluation method is proposed to evaluate the performance of the two teams. Based on the evaluation results, the game designers can adjust the abilities of the characters so as to achieve team balance. We demonstrate TABS for our in-house MagePowerCraft game in which each team consists of up to three characters.