  End-users' trust in automated agents is important as automated
decision-making and planning is increasingly used in many aspects of people's
lives. In real-world applications of planning, multiple optimization objectives
are often involved. Thus, planning agents' decisions can involve complex
tradeoffs among competing objectives. It can be difficult for the end-users to
understand why an agent decides on a particular planning solution on the basis
of its objective values. As a result, the users may not know whether the agent
is making the right decisions, and may lack trust in it. In this work, we
contribute an approach, based on contrastive explanation, that enables a
multi-objective MDP planning agent to explain its decisions in a way that
communicates its tradeoff rationale in terms of the domain-level concepts. We
conduct a human subjects experiment to evaluate the effectiveness of our
explanation approach in a mobile robot navigation domain. The results show that
our approach significantly improves the users' understanding, and confidence in
their understanding, of the tradeoff rationale of the planning agent.
