  With Artificial Intelligence on the rise, human interaction with autonomous
agents becomes more frequent. Effective human-agent collaboration requires
users to understand the agent's behavior, as failing to do so may cause reduced
productivity, misuse or frustration. Agent strategy summarization methods are
used to describe the strategy of an agent to its destined user through
demonstration. A summary's objective is to maximize the user's understanding of
the agent's aptitude by showcasing its behaviour in a selected set of world
states. While shown to be useful, we show that current methods are limited when
tasked with comparing between agents, as each summary is independently
generated for a specific agent. In this paper, we propose a novel method for
generating dependent and contrastive summaries that emphasize the differences
between agent policies by identifying states in which the agents disagree on
the best course of action. We conduct user studies to assess the usefulness of
disagreement-based summaries for identifying superior agents and conveying
agent differences. Results show disagreement-based summaries lead to improved
user performance compared to summaries generated using HIGHLIGHTS, a strategy
summarization algorithm which generates summaries for each agent independently.
