Generic opponent modelling approach for real time strategy games

One of the fundamental and challengeable research areas in Real Time Strategy (RTS) games is opponent modelling. Most current approaches to opponent modelling pretended inefficiency. They are either computationally expensive or required a numerous amount of online gameplays to start learn successful models. Unfortunately, most successful approaches also were game specific. They mainly depend on the expert's knowledge of the game. In this paper, a generic and adaptive opponent modelling approach for RTS games is proposed. It is a completely automated approach for learning the highly informative features of the opponent's behavior of any RTS game. Inspired by the case-based reasoning technique, a case base of different opponent models is constructed in the approach offline phase. The online phase (during gameplay) utilizes only this model base for opponent classification. To better cope with opponents that switch strategies, the approach keeps track of the performance after classification. To show how the proposed approach is beneficial, a case study called SPRING game case-study is presented.