A Study on Training and Developing Large Language Models for Behavior Tree Generation

TL;DR

The paper investigates using large language models (LLMs) to automatically generate behavior trees (BTs) for complex tasks, addressing the inefficiency and domain-expertise requirements of manual BT design. It introduces a BTGen framework encompassing data synthesis, model training (pretraining and supervised fine-tuning), and an agent-based development pipeline, including a Monte Carlo Tree Search–inspired data generation method and a multi-module BTGen Agent (Memory, Action, Planning, Profile, plus Refinement). A rigorous verification and validation (V&V) pipeline is proposed to ensure BT executability and task performance, leveraging unit tests, simulators, and LLM-based world models. The work also details data schemas, node libraries, prompting strategies, and application architectures to facilitate real-world deployment and evaluation. Collectively, the framework aims to deliver scalable, reliable, and interpretable BT generation guided by rigorous V&V, with a clear path for future enhancements in data quality, training strategies, and simulator-augmented validation.

Abstract

This paper presents an innovative exploration of the application potential of large language models (LLM) in addressing the challenging task of automatically generating behavior trees (BTs) for complex tasks. The conventional manual BT generation method is inefficient and heavily reliant on domain expertise. On the other hand, existing automatic BT generation technologies encounter bottlenecks related to task complexity, model adaptability, and reliability. In order to overcome these challenges, we propose a novel methodology that leverages the robust representation and reasoning abilities of LLMs. The core contribution of this paper lies in the design of a BT generation framework based on LLM, which encompasses the entire process, from data synthesis and model training to application developing and data verification. Synthetic data is introduced to train the BT generation model (BTGen model), enhancing its understanding and adaptability to various complex tasks, thereby significantly improving its overall performance. In order to ensure the effectiveness and executability of the generated BTs, we emphasize the importance of data verification and introduce a multilevel verification strategy. Additionally, we explore a range of agent design and development schemes with LLM as the central element. We hope that the work in this paper may provide a reference for the researchers who are interested in BT generation based on LLMs.

Figures (7)

• Figure 1: The overview structure of our paper.
• Figure 2: A brief illustration for the evolution of GPT-series models zhao2023survey.
• Figure 3: Illustration of the BT Generation method utilizing a framework inspired by Monte Carlo Tree Search (MCTS) for generating BTs. Each state represents a BT, with transitions informed by selection, expansion, validation, and iterative refinement processes. The approach integrates a language model for decision-making and node expansion.
• Figure 4: Illustration of the BTGen Agent framework.
• Figure 5: An example for the HumanEval benchmark. The prompt provided to the model is shown in green, and a successful model-generated completion is shown in orange.