# Mimicking Playstyle by Adapting Parameterized Behavior Trees in RTS Games

arXiv: https://arxiv.org/abs/2111.12144 · HTML: https://mlsanity.com/paper/2111.12144/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 27 references that are in the map.
- First posted on arXiv 23 Nov 2021.

## Builds on (most cited first, 3 of 3)

- [Behavior Trees in Robotics and AI: An Introduction](https://mlsanity.com/paper/1709.00084/) (2017)
- [Imitation Learning with Concurrent Actions in 3D Games](https://mlsanity.com/paper/1803.05402/) (2018)
- [Learning Macromanagement in StarCraft from Replays using Deep Learning](https://mlsanity.com/paper/1707.03743/) (2017)

## Built on by (most cited first, 1 of 1)

- [SAT-RTS: A systematic framework for tactical knowledge extraction and visualization-based analysis in real-time strategy games](https://mlsanity.com/paper/2606.30090/) (2026)

## Abstract (arXiv)

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.