Abstract
In this article, we discuss a method for testing policies that guide
groups of agents in simulations for interactions with other agents and
the environment that reveal weaknesses of these policies. Our method
is based on learning interaction sequences using particle swarm systems
and has as one crucial component so-called goal ordering structures
that are used to guide the learning towards weakness-revealing
interactions. Our discussion centers around the different ways a new
measuring idea can be integrated into such an ordering structure using
the example of testing patrol and interception policies for harbours.
Our experimental evaluation reveals that the position of placement of
a new measure in an existing ordering structure can greatly influence
the testing results, positively and negatively, but mostly mirrors the
intuition associated with the placement.
Refereed
No
