Tan, Tse Guan and Patricia Anthony and Teo, Jason Tze Wi and Ong, Jia Hui (2011) Neural network ensembles for video game AI using evolutionary multi-objective optimization. In: 11th International Conference on Hybrid Intelligent Systems, HIS 2011, 5-8 December 2011, Malacca, Malaysia.
Full text not available from this repository.Abstract
Recently, there has been an increasing interest in game artificial intelligence (AI). Game AI is a system that makes the game characters behave like human beings that is able to make smart decisions to achieve the target in a computer or video game. Thus, this study focuses on an automated method of generating artificial neural network (ANN) controller that is able to display good playing behaviors for a commercial video game. In this study, we create neural-based game controller for screen-capture of Ms. Pac-Man using a multi-objective evolutionary algorithm (MOEA) for training or evolving the architectures and connection weights (including biases) in ANN corresponding to conflicting goals of minimizing complexity in ANN and maximizing Ms. Pac-man game score. In particular, we have chosen the commonly-used Pareto Archived Evolution Strategy (PAES) algorithm for this purpose. After the entire training process is completed, the controller is tested for generalization using the optimized networks in single network (single-net) and neural network ensemble (multi-net) environments. The multi-net model is compared to single-net model, and the results reveal that neural network ensemble is able learn to play with good strategies in a complex, dynamic and difficult game environment which is not achievable by the individual neural network.
| Item Type: | Conference or Workshop Item (UNSPECIFIED) |
|---|---|
| Keyword: | Ms. Pac-man, Multi-objective evolutionary algorithm, Neural network ensembles, Pareto Archived Evolution Strategy, Winner-takes-all |
| Subjects: | Q Science > QA Mathematics |
| Department: | SCHOOL > School of Engineering and Information Technology |
| Depositing User: | ADMIN ADMIN |
| Date Deposited: | 12 Jul 2012 12:44 |
| Last Modified: | 30 Dec 2014 09:42 |
| URI: | https://eprints.ums.edu.my/id/eprint/4506 |
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