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