Evaluating Different Genetic Operators in the Testing for Unwanted Emergent Behavior Using Evolutionary Learning of Behavior
Evaluating Different Genetic Operators in the Testing for Unwanted Emergent Behavior Using Evolutionary Learning of Behavior
复制标题
使用行为进化学习来评估不同的遗传算子来测试不需要的突发行为
DOI：
10.1109/iat.2006.63
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发表时间：
2006
期刊：
影响因子：
--
通讯作者：
Jordan Kidney
中科院分区：
文献类型：
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作者：
J. Denzinger;Jordan Kidney
我们提出了一个实验比较不同的遗传算子，它们的使用在进化学习方法，搜索不需要的紧急行为在多智能体系统。该学习方法的思想是进化一组所谓的攻击代理的合作行为，这些攻击代理在与被测代理相同的环境中起作用。攻击代理使用动作序列作为代理体系结构，并且一组这样的代理的质量通过它们的行为使被测试代理显示出不想要的行为的接近程度来测量。我们的实验中的战神II救援模拟器与代理团队的学生写的，这种方法是能够找到不需要的紧急行为的代理。他们还表明，相当标准的遗传算子（在团队层面和代理层面）已经足以发现这种不必要的行为。
We present an experimental comparison of different genetic operators regarding their use in an evolutionary learning method that searches for unwanted emergent behavior in a multi-agent system. The idea of the learning method is to evolve cooperative behavior of a group of so-called attack agents that act in the same environment as the tested agents. The attack agents use action sequences as agent architecture and the quality of a group of such agents is measured by how near their behavior brings the tested agents to show the unwanted behavior. Our experiments within the ARES II rescue simulator with an agent team written by students show that this method is able to find unwanted emergent behavior of the agents. They also show that rather standard genetic operators (on the team level and the agent level) are already sufficient to find this unwanted behavior.