A Study on Training and Developing Large Language Models for Behavior
  Tree Generation
Fu Li Xueying Wang Bin Li Yunlong Wu Yanzhen Wang Xiaodong Yi
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.