BT-D2: A Hierarchical Combat Task Decomposition Framework with Dynamic Constraint Injection in Behavior Tree

This paper addresses challenges such as semantic gaps, logical inconsistencies, and neglect of domain knowledge in task decomposition for complex systems, especially in military command scenarios. We propose the BT-D2 framework — a behavior tree-guided method that integrates large language models (LLMs) with structured domain knowledge to enable dynamic and executable task decomposition. By embedding military doctrines and equipment parameters into hierarchical behavior tree nodes, the framework creates a constraint-based template for transforming high-level instructions into tactical action sequences. Key components include an extended behavior tree architecture for dynamic parameter binding, a multi-agent collaborative workflow for strategic-to-tactical planning, and an adaptive mechanism for semantic alignment and conflict resolution. Experimental results demonstrate that BT-D2 outperforms baseline methods (CoT, ReAct, GTN) in semantic integrity, decomposition diversity, and structural rationality across 200 combat scenarios. Ablation studies validate the critical role of behavior trees in enhancing task executability and hierarchical architecture in improving planning efficiency. This work contributes a novel framework for bridging generative AI with domain-specific constraints, offering promising applications in military planning, logistics, and intelligent systems.