Research progress and Application of Behavior Tree Technology Artificial intelligence will not only change the production and life of human beings, but also fundamentally change the winning mechanism and fighting mode of modern wars, and give birth to new fighting means and ideas. Behavior tree will be a good medium for the application of artificial intelligence in the military field. This paper first introduces the basic principle of Behavior Tree (BT), and then elaborates and analyzes the development status of Behavior Tree from the aspects of implementation and design application. Secondly, the characteristics of behavior tree, finite state machine and hierarchical finite state machine are compared from the aspects of hierarchy, maintainability, code coupling, expansibility and reusability. Finally, the development trend of behavior tree combined with machine learning and expert system and its application prospects in interactive software platform, unmanned autonomous systems and combat simulation systems are analyzed.

Analysis and Exploitation of Synchronized Parallel Executions in Behavior Trees Behavior Trees (BTs) are becoming a popular tool to model the behaviors of autonomous agents in the computer game and the robotics industry. One of the key advantages of BTs lies in their composability, where complex behaviors can be built by composing simpler ones. The parallel composition is the one with the highest potential since the complexity of composing preexisting behaviors in parallel is much lower than the one needed using classical control architectures as finite state machines. However, the parallel composition is rarely used due to the underlying concurrency problems that are similar to the ones faced in concurrent programming. In this paper, we define two synchronization techniques to tackle the concurrency problems in BTs compositions and we show how to exploit them to improve behavior predictability. Also, we introduce measures to assess execution performance and we show how design choices can affect them. To illustrate the proposed framework, we provide a set of experiments using the R1 robot and we gather statisticallysignificant data.