# Learning of Behavior Trees for Autonomous Agents

arXiv: https://arxiv.org/abs/1504.05811 · HTML: https://mlsanity.com/paper/1504.05811/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2015.

## Built on by (most cited first, 14 of 14)

- [LMAgent: A Large-scale Multimodal Agents Society for Multi-user Simulation](https://mlsanity.com/paper/2412.09237/) (2024)
- [Behavior Trees in Robot Control Systems](https://mlsanity.com/paper/2203.13083/) (2022)
- [Integrating Intent Understanding and Optimal Behavior Planning for Behavior Tree Generation from Human Instructions](https://mlsanity.com/paper/2405.07474/) (2024)
- [Combining Planning and Learning of Behavior Trees for Robotic Assembly](https://mlsanity.com/paper/2103.09036/) (2021)
- [Modeling Earth-Scale Human-Like Societies with One Billion Agents](https://mlsanity.com/paper/2506.12078/) (2025)
- [KT-BT: A Framework for Knowledge Transfer Through Behavior Trees in Multi-Robot Systems](https://mlsanity.com/paper/2209.02886/) (2022)
- [Learning of Parameters in Behavior Trees for Movement Skills](https://mlsanity.com/paper/2109.13050/) (2021)
- [HBTP: Heuristic Behavior Tree Planning with Large Language Model Reasoning](https://mlsanity.com/paper/2406.00965/) (2024)
- [EvolvingBehavior: Towards Co-Creative Evolution of Behavior Trees for Game NPCs](https://mlsanity.com/paper/2209.01020/) (2022)
- [Verifying Safety of Behaviour Trees in Event-B](https://mlsanity.com/paper/2209.14045/) (2022)
- [MRBTP: Efficient Multi-Robot Behavior Tree Planning and Collaboration](https://mlsanity.com/paper/2502.18072/) (2025)
- [Behavior Tree-Based Task Planning for Multiple Mobile Robots using a Data Distribution Service](https://mlsanity.com/paper/2201.10918/) (2022)
- [Intelligent Framework for Human-Robot Collaboration: Dynamic Ergonomics and Adaptive Decision-Making](https://mlsanity.com/paper/2503.07901/) (2025)
- [Contract-Grounded Behavior Tree Synthesis via Coding Agents](https://mlsanity.com/paper/2607.12220/) (2026)

## Abstract (arXiv)

Definition of an accurate system model for Automated Planner (AP) is often impractical, especially for real-world problems. Conversely, off-the-shelf planners fail to scale up and are domain dependent. These drawbacks are inherited from conventional transition systems such as Finite State Machines (FSMs) that describes the action-plan execution generated by the AP. On the other hand, Behavior Trees (BTs) represent a valid alternative to FSMs presenting many advantages in terms of modularity, reactiveness, scalability and domain-independence. In this paper, we propose a model-free AP framework using Genetic Programming (GP) to derive an optimal BT for an autonomous agent to achieve a given goal in unknown (but fully observable) environments. We illustrate the proposed framework using experiments conducted with an open source benchmark Mario AI for automated generation of BTs that can play the game character Mario to complete a certain level at various levels of difficulty to include enemies and obstacles.