一句话：提出 EffBT，从 GR(1) 规约自动合成语义正确且高效执行的行为树，并引入剪枝与 Parallel 节点优化执行。
行为树因模块化与反应性而被机器人和软件工程广泛采用，但自动合成语义正确、结构良好且高效可执行的行为树仍具挑战。论文提出 EffBT，一种面向行为树的反应式合成方法：在 GR(1) 规约的可实现性检查过程中，从中间策略可靠地构造行为树，从而保证生成控制器的正确性。为提升执行效率，EffBT 还引入剪枝策略并使用 Parallel 节点，这些在先前工作中尚未被探索。作者证明了方法的可靠性，并在多种场景与数据集上实验，结果表明其有效性与效率。
Behavior Trees (BTs), originated from the control of Non-Player-Characters (NPCs), have been widely embraced in robotics and software engineering communities due to their modularity, reactivity, and other beneficial characteristics. It is highly desirable to synthesize BTs automatically. The consequent challenges are to ensure the generated BTs semantically correct, well-structured, and efficiently executable. To address these challenges, in this paper, we present a novel reactive synthesis method for BTs, namely EffBT, to generate correct and efficient controllers from formal specifications in GR(1) automatically. The idea is to construct BTs soundly from the intermediate strategies derived during the algorithm of GR(1) realizability check. Additionally, we introduce pruning strategies and use of Parallel nodes to improve BT execution, while none of the priors explored before. We prove the soundness of the EffBT method, and the experimental results demonstrate its effectiveness in various scenarios and datasets.
DOI 原文 ·
@inproceedings{paperbot8925,
title = {EffBT: An Efficient Behavior Tree Reactive Synthesis and Execution Framework},
author = {Ziji Wu and Yu Huang and Peishan Huang and Shanghua Wen and Minglong Li and Ji Wang},
booktitle = {ICSE},
year = {2025},
doi = {10.1109/icse55347.2025.00225}
}