Related papers: Mimicking Playstyle by Adapting Parameterized Behavior Trees in RTS Games

Mimicking Playstyle by Adapting Parameterized Behavior Trees in RTS Games

URL: http://arxiv.org/abs/2111.12144v1
Date: Tue, 23 Nov 2021 20:36:28 GMT
Title: Mimicking Playstyle by Adapting Parameterized Behavior Trees in RTS Games
Authors: Andrzej Kozik, Tomasz Machalewski, Mariusz Marek, Adrian Ochmann
Abstract summary: Behavior Trees (BTs) impacted the field of Artificial Intelligence (AI) in games. BTs forced complexity of handcrafted BTs to became barely-tractable and error-prone. Recent trends in the field focused on automatic creation of AI-agents. We present a novel approach to semi-automatic construction of AI-agents, that mimic and generalize given human gameplays.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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