Computable Expert Knowledge in Computer Games

Abstract

We algorithmically compute and demonstrate multi-scale expert knowledge of computer gaming through pattern compositions on two levels of heterogeneity. Hierarchical clustering (HC) is applied to construct block-based heatmaps: colored matrices framed by two hierarchical trees imposed upon row and column axes. The computed heterogeneity is seen to induce different collections of viable gaming features pertaining to different map-clusters. On the game level, the map-dependent heterogeneity is seen to reveal which gaming-feature-pattern compositions are indeed viable for wins or losses with near-certainty, and which correspond to 50-50 uncertainty in outcome. Hence, such pattern compositions become the critical knowledge bases for pre-game prediction as well as ongoing-gaming strategy. The computer game, TagPro: Capture the Flag, is used as an illustrating example throughout the development of this paper.