  With Artificial Intelligence on the rise, human interaction with autonomous
agents becomes more frequent. Effective human-agent collaboration requires that
the human understands the agent's behavior, as failing to do so may lead to
reduced productiveness, misuse, frustration and even danger. Agent strategy
summarization methods are used to describe the strategy of an agent to its
destined user through demonstration. The summary's purpose is to maximize the
user's understanding of the agent's aptitude by showcasing its behaviour in a
set of world states, chosen by some importance criteria. While shown to be
useful, we show that these methods are limited in supporting the task of
comparing agent behavior, as they independently generate a summary for each
agent. In this paper, we propose a novel method for generating contrastive
summaries that highlight the differences between agent's policies by
identifying and ranking states in which the agents disagree on the best course
of action. We conduct a user study in which participants face an agent
selection task. Our results show that the novel disagreement-based summaries
lead to improved user performance compared to summaries generated using
HIGHLIGHTS, a previous strategy summarization algorithm.
