In recent years, computer games have become a common form of entertainment. Fast advancement in computer technology and internet speed have helped entertainment software developers to create graphical games that keep a variety of players’ interest. The emergence of artificial intelligence systems has evolved computer gaming technology in new and profound ways. Artificial intelligence provides the illusion of intelligence in the behavior of NPCs (Non-Playable-Characters). NPCs are able to use the increased CPU, GPU, RAM, Storage and other bandwidth related capabilities, resulting in very difficult game play for the end user. In many cases, computer abilities must be toned down in order to give the human player a competitive chance in the game. This improves the human player’s perception of fair game play and allows for continued interest in playing. A proper adaptive learning mechanism is required to further this human player’s motivation. During this project, past achievements of adaptive learning on computer chess game play are reviewed and adaptive learning mechanisms in computer chess game play is proposed. Adaptive learning is used to adapt the game’s difficulty level to the players’ skill levels. This adaptation is done using the player’s game history and current performance. The adaptive chess game is implemented through the open source chess game engine Beowulf, which is freely available for download on the internet.
| Identifer | oai:union.ndltd.org:csusb.edu/oai:scholarworks.lib.csusb.edu:etd-1255 |
| Date | 01 September 2015 |
| Creators | Peiravi, Mehdi |
| Publisher | CSUSB ScholarWorks |
| Source Sets | California State University San Bernardino |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses, Projects, and Dissertations |
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