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The discovery of Behavior Trees (BTs) impacted the field of ArtificialIntelligence (AI) in games, by providing flexible and natural representation ofnon-player characters (NPCs) logic, manageable by game-designers. Nevertheless,increased pressure on ever better NPCs AI-agents forced complexity ofhandcrafted BTs to became barely-tractable and error-prone. On the other hand,while many just-launched on-line games suffer from player-shortage, theexistence of AI with a broad-range of capabilities could increase playersretention. Therefore, to handle above challenges, recent trends in the fieldfocused on automatic creation of AI-agents: from deep- andreinforcementlearning techniques to combinatorial (constrained) optimizationand evolution of BTs. In this paper, we present a novel approach tosemi-automatic construction of AI-agents, that mimic and generalize given humangameplays by adapting and tuning of expert-created BT under a developedsimilarity metric between source and BT gameplays. To this end, we formulatedmixed discrete-continuous optimization problem, in which topological andfunctional changes of the BT are reflected in numerical variables, andconstructed a dedicated hybrid-metaheuristic. The performance of presentedapproach was verified experimentally in a prototype real-time strategy game.Carried out experiments confirmed efficiency and perspectives of presentedapproach, which is going to be applied in a commercial game.

Mimicking Playstyle by Adapting Parameterized Behavior Trees in RTS Games