# Auction-Based Behavior Tree Evolution for Heterogeneous Multi-Agent Systems
## 5 Conclusions  
The performance of heterogeneous MASs in multi-task environments, especially those with numerous high-priority tasks, has become a key concern. In order to help MASs execute tasks autonomously, efficiently, and collaboratively, this paper designed a hierarchical framework based on BT for planning and control, called Auction-Based Behavior Tree Evolution (ABTE). This framework combines the command layer and an execution layer, ensuring efficient task allocation and automatic behavior generation. The experiments are designed around a typical Disaster Rescue Scenario, where heterogeneous multi-agents are grouped according to different functions. The effectiveness of the framework is verified by the performance of MASs facing numerous tasks with different priorities. The experimental results indicate that, compared with the current methods, the three-way handshaking communication protocol in ABTE can significantly maximize the advantages of robots and improve resource utilization within heterogeneous MASs. Furthermore, the grammatical evolution algorithm can not only generate execution behaviors tailored to different tasks but also enable MASs to preferentially complete tasks with higher priority.  
It is worth noting that the Disaster Rescue Scenario represents just one application for the hierarchical behavior control framework proposed in this paper. This framework, which stratifies command and execution behaviors, is a paradigm that utilizes the modularity of BT, allowing the tree to expand and prune dynamically according to varying tasks. For heterogeneous MASs, the introduction of this framework can significantly boost adaptability, synergy, and autonomy. However, when implementing ABTE in alternative applications, it is crucial for researchers to ascertain that all tasks within the environment are known. Such knowledge provides the foundational basis for task allocation at the command layer. Furthermore, as assumed in Disaster Rescue Scenarios, the energy utilized by robots in performing specific tasks ought to surpass the energy expended during traveling. This premise is based on the emphasis of ABTE on optimizing the sequence of task execution rather than shortening the path. Future efforts will focus on refining robot behaviors to explore the diversity of evolutionary BTs and enhancing the task bidding value computation model. The integration of planning algorithms for minimization and enhancement will also be a key focus.  
[PERSON] and [PERSON] participated in the conception and design of the work; [PERSON] and [PERSON] investigated and designed the methodology; [PERSON] designed the software and experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] wrote original draft; [PERSON], [PERSON] and [PERSON] edited the paper; [PERSON] and [PERSON] administrated the project. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant Nos. 62106278 and 91948303-1), the National Natural Science Foundation of China (Grant Nos. 611803375, 12002380, and 62101575), the National Key R&D Program of China (Grant No. 2021 ZD0140301), and the Postgraduate Scientific Research Innovation Project of Hunan Province (Grant No. QL20210018).  
Not applicable.  
The data presented in this study are openly available in ABTE at [[https://github.com/shanghuaw/ABTE](https://github.com/shanghuaw/ABTE)]([https://github.com/shanghuaw/ABTE](https://github.com/shanghuaw/ABTE)).  
The authors declare no conflicts of interest.