Cheah, Keei Yuan (2015) Improving tower defense game AI (Differential evolution vs evolutionary programming). Universiti Malaysia Sabah. (Unpublished)
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Abstract
The use of Artificial Intelligence has emerged into every corner of our daily life. In this modern technology era, there are many 3-Dimensional games are developed with Artificial Intelligence methods to bring out a better gaming experience. The most used of Artificial Intelligence in gaming environment is Real Time Strategy games which Real Time means actual time during a process whereas Strategy means a set of different skills. Tower Defense games are one of the Real Time Strategy category which human players exert their gameplay strategy to build tower and win highest level of game. Research on implementing Artificial Intelligence to Tower Defense games are seems unpopular in the world but Tower Defense games have been proven that its simplicity and availability to create a test bed for research. Most of the research used it as a testbed for comparing the performances of proposed algorithms. This research aims to compare the performance of Evolutionary Algorithms comprising of Differential Evolution and Evolutionary Programming combined Jordan Recurrent Neural Network, Elman’s Recurrent Neural Network, Feed Forward Neural Network, and Ensemble Neural Network. The results showed the performance for Differential Evolution outperformed Evolutionary Programming. By comparing the Artificial Neural Networks, the Ensemble Neural Network proved to be slightly better than other Artificial Neural Networks. The combination of Differential Evolution and Ensemble Neural Network generated better results compared to other combination.
| Item Type: | Academic Exercise |
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| Keyword: | Artificial Intelligence, Real Time Strategy, Tower Defense games, 3-Dimensional games, gaming experience |
| Subjects: | ?? QA76 ?? |
| Department: | FACULTY > Faculty of Computing and Informatics |
| Depositing User: | ADMIN ADMIN |
| Date Deposited: | 06 Nov 2015 11:36 |
| Last Modified: | 27 Oct 2017 15:14 |
| URI: | https://eprints.ums.edu.my/id/eprint/12107 |
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