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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Salge, C. Mahlmann, T. |
| Copyright Year | 2010 |
| Description | Author affiliation: Department of Computer Science, University of Hertfordshire, Hatfield, United Kingdom (Salge, C.) || Center for Computer Games Research, ITU Copenhagen, Denmark (Mahlmann, T.) |
| Abstract | We present a new approach to use adaptive AI and Information Theory to aid the evaluation of game mechanics. Being able to evaluate the core game mechanics early during production is useful to improve the quality of a game, and ultimately, player satisfaction. A current problem with automated game evaluation via AI is to define measurable parameters that correlate to the quality of the game mechanics. We apply the Information Theory based concept of “Relevant Information” to this problem and argue that there is a relation between enjoyment related game-play properties and Relevant Information for an AI playing the game. We also demonstrate, with a simple game implementation, a.) how an adaptive AI can be used to approximate the Relevant Information, b.) how those measurable numerical values relate to certain game design flaws c.) how this knowledge can be used to improve the game. |
| Starting Page | 281 |
| Ending Page | 288 |
| File Size | 2138781 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424462957 |
| e-ISBN | 9781424462964 |
| DOI | 10.1109/ITW.2010.5593344 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-08-18 |
| Publisher Place | Denmark |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Information Theory Strategic Games Games Artificial neural networks Entropy Random variables Artificial intelligence Game Development Mutual information AI and Games |
| Content Type | Text |
| Resource Type | Article |
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