OPPONENT MODELS PREPROCESSING IN REAL-TIME STRATEGY GAMES | ||||
| International Journal of Intelligent Computing and Information Sciences | ||||
| Article 3, Volume 16, Issue 3, July 2016, Page 37-45 PDF (1.53 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ijicis.2016.19835 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| M. Mourad; M. Aref; M. Abd-Elaziz | ||||
| Computer Science and Basic Sciences Department, Faculty of Computer and Information Sciences, AM Shams University, | ||||
| Abstract | ||||
| Creating a human-like computer player in real-time strategy games requires huge number of opponent models, these models must be preprocessed to either focus on accuracy or performance according to our needs. In order to preprocess these models accurately, we need to detect their type. Opponent models' type can be complex or simple. Complex opponent models are low variance models whose differences in features' values are low, so in order to accurately separate between these models, we need to preprocess them by increasing their dimensions. Simple opponent models are high variance models whose differences in features' values are high, so in order to separate between these models in a reasonable time, we need to preprocess them to decrease their dimensions, if possible, without accuracy or data loss. | ||||
| Keywords | ||||
| Opponent modeling; adaboost; Rough Sets; Clustering; real-time strategy games; ITS | ||||
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