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

摘要

We present a general scheme for testing multi-agent 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.