Mimicking Playstyle by Adapting Parameterized Behavior Trees in RTS Games

TL;DR

This paper introduces a semi-automatic method to adapt behavior trees in RTS games, enabling NPCs to mimic human playstyles by optimizing tree structures using hybrid metaheuristics.

Contribution

It presents a novel hybrid optimization approach for tuning behavior trees to replicate human gameplay, addressing complexity issues in handcrafted AI.

Findings

Effective in mimicking human playstyles in RTS games

Validated through experiments in a prototype game

Shows potential for commercial game integration

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.