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.

翻译：本文创新性地探索了大语言模型（LLM）在解决复杂任务自动生成行为树（BTs）这一挑战性任务中的应用潜力。传统人工生成BT的方法效率低下且高度依赖领域专家知识，而现有自动生成BT技术则面临任务复杂性、模型适应性与可靠性方面的瓶颈。为克服这些挑战，我们提出了一种利用LLM强大表征与推理能力的新方法。本文的核心贡献在于设计并实现了一个基于LLM的BT生成框架，该框架涵盖了从数据合成、模型训练到应用开发与数据验证的全流程。通过引入合成数据来训练行为树生成模型（BTGen模型），增强了模型对各类复杂任务的理解与适应能力，从而显著提升了整体性能。为确保生成BT的有效性与可执行性，我们强调了数据验证的重要性，并引入了一种多层次验证策略。此外，我们还探索了一系列以LLM为核心的智能体设计与开发方案。希望本文工作能为关注基于LLM的BT生成的研究者提供参考。