LSA-based Semantic Representation of Action Games

Modeling the semantic space of a complex dynamic domain, like an action game, by automatically identifying the relations governing the game’s concepts, entities, actions and other features, is a challenging research objective. In this paper we propose modeling the semantic space of the action game SpaceDebris, in order to identify semantic similarities between players’ gaming styles. To this end we employ Latent Semantic Analysis and attempt to identify latent underlying semantic information governing the various gaming techniques. The several challenging research issues that arise when attempting to apply Latent Semantic Analysis to non-textual data describing a complex dynamic problem space (defining the semantic vocabulary and “word” utterances, deciding upon the dimensionality reduction rate, etc.) are addressed, and the framework of the proposed experimental setup is described. The extracted similarities are further employed for player modelling, i.e. grouping players according to their playing styles.