  As advances in artificial intelligence enable increasingly capable
learning-based autonomous agents, it becomes more challenging for human
observers to efficiently construct a mental model of the agent's behaviour. In
order to successfully deploy autonomous agents, humans should not only be able
to understand the individual limitations of the agents but also have insight on
how they compare against one another. To do so, we need effective methods for
generating human interpretable agent behaviour summaries. Single agent
behaviour summarization has been tackled in the past through methods that
generate explanations for why an agent chose to pick a particular action at a
single timestep. However, for complex tasks, a per-action explanation may not
be able to convey an agents global strategy. As a result, researchers have
looked towards multi-timestep summaries which can better help humans assess an
agents overall capability. More recently, multi-step summaries have also been
used for generating contrasting examples to evaluate multiple agents. However,
past approaches have largely relied on unstructured search methods to generate
summaries and require agents to have a discrete action space. In this paper we
present an adaptive search method for efficiently generating contrasting
behaviour summaries with support for continuous state and action spaces. We
perform a user study to evaluate the effectiveness of the summaries for helping
humans discern the superior autonomous agent for a given task. Our results
indicate that adaptive search can efficiently identify informative contrasting
scenarios that enable humans to accurately select the better performing agent
with a limited observation time budget.
