Competitive Algorithms for Coevolving Both Game Content and AI. A Case Study: Planet Wars

The classical approach of competitive coevolution (CC) applied in games tries to exploit an arms race between coevolving populations that belong to the same species (or at least to the same biotic niche), namely strategies, rules, tracks for racing, or any other. This paper proposes the coevolution of entities belonging to different realms (namely biotic and abiotic) via a competitive approach. More precisely, we aim to coevolutionarily optimize both virtual players and game content. From a general perspective, our proposal can be viewed as a method of procedural content generation combined with a technique for generating game AI. This approach can not only help game designers in game creation but also generate content personalized to both specific players' profiles and game designer's objectives (e.g., create content that favors novice players over skillful players). As a case study we use Planet Wars, the real-time strategy (RTS) game associated with the 2010 Google AI Challenge contest, and demonstrate (via an empirical study) the validity of our approach.