论文标题
使用语法进化有效地演变为PPA风格的行为树
Efficiently Evolving Swarm Behaviors Using Grammatical Evolution With PPA-style Behavior Trees
论文作者
论文摘要
通过人造代理不断发展的群体行为在计算上昂贵且具有挑战性。由于奖励结构在群体问题中通常很少，因此数百个模拟在进化成功的群体行为中只有少数模拟。此外，群体进化算法通常依赖于临时健身结构，并且需要为每个群任务设计新颖的健身功能。本文通过系统地结合后条件 - 前提行动（PPA）规范行为树（BT）和语法演化来发展群的行为。 PPA结构用系统的后条件检查代替了临时奖励结构，该检查允许一种通用语法可以仅使用环境提示和BT反馈来学习不同任务的解决方案。学习行为的静态性能很差，因为没有代理人学会所有必要的子任务，但是在发展的同时，性能是出色的，因为代理可以在新环境中迅速改变行为。不断发展的算法在75％的学习试验中成功进行了觅食和巢穴维护任务，这对先前的工作有了8倍的改善。
Evolving swarm behaviors with artificial agents is computationally expensive and challenging. Because reward structures are often sparse in swarm problems, only a few simulations among hundreds evolve successful swarm behaviors. Additionally, swarm evolutionary algorithms typically rely on ad hoc fitness structures, and novel fitness functions need to be designed for each swarm task. This paper evolves swarm behaviors by systematically combining Postcondition-Precondition-Action (PPA) canonical Behavior Trees (BT) with a Grammatical Evolution. The PPA structure replaces ad hoc reward structures with systematic postcondition checks, which allows a common grammar to learn solutions to different tasks using only environmental cues and BT feedback. The static performance of learned behaviors is poor because no agent learns all necessary subtasks, but performance while evolving is excellent because agents can quickly change behaviors in new contexts. The evolving algorithm succeeded in 75\% of learning trials for both foraging and nest maintenance tasks, an eight-fold improvement over prior work.
