Design and Evaluation of an Extended Learning Classifier-Based StarCraft Micro AI

Due to the manifold challenges that arise when developing an artificial intelligence that can compete with human players, the popular realtime-strategy game Starcraft: Broodwar (BW) has received attention from the computational intelligence research community. It is an ideal testbed for methods for self-adaption at runtime designed to work in complex technical systems. In this work, we utilize the broadlys-used Extended Classifier System (XCS) as a basis to develop different models of BW micro AIs: the Defender, the Attacker, the Explorer and the Strategist. We evaluate theses AIs with a focus on their adaptive and co-evolutionary behaviors. To this end, we stage and analyze the outcomes of a tournament among the proposed AIs and we also test them against a non-adaptive player to provide a proper baseline for comparison and learning evolution. Of the proposed AIs, we found the Explorer to be the best performing design, but, also that the Strategist shows an interesting behavioral evolution.

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