首页> 外文会议>IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA 2009) >Testing harbour patrol and interception policies using particle-swarm-based learning of cooperative behavior
【24h】

Testing harbour patrol and interception policies using particle-swarm-based learning of cooperative behavior

机译:使用基于粒子群的合作行为学习测试港口巡逻和拦截策略

获取原文

摘要

We present a general scheme for testing multiagent systems, respectively policies used by them, for unwanted emergent behavior using learning of cooperative behavior via particle swarm systems. By using particle swarm systems in this setting, we are able to create agents interacting/attacking the tested agents that can use parameterised high-level actions. We also can evaluate the quality of an attack using several measures that can be prioritised and used in a multi-objective manner in the search. This solves some general problems of other testing approaches using learning. We instantiate this general scheme to test harbour patrol and interception policies for two Canadian harbours, showing that our approach is able to find problems in these policies.
机译:我们提出了一种通用方案,用于通过使用粒子群系统学习协作行为来测试多代理系统(分别由其使用的策略)是否有不需要的紧急行为。通过在此设置中使用粒子群系统,我们能够创建可使用参数化高级操作来交互/攻击已测试代理的代理。我们还可以使用几种可以评估优先级的措施来评估攻击的质量,这些措施可以在搜索中以多目标的方式使用。这解决了使用学习的其他测试方法的一些一般问题。我们实例化了这个通用方案,以测试两个加拿大港口的港口巡逻和拦截政策,这表明我们的方法能够在这些政策中发现问题。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号