# Tradeoff-Focused Contrastive Explanation for MDP Planning

arXiv: https://arxiv.org/abs/2004.12960 · HTML: https://mlsanity.com/paper/2004.12960/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2020.

## Built on by (most cited first, 5 of 5)

- [Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey](https://mlsanity.com/paper/2108.09003/) (2021)
- [Levels of explainable artificial intelligence for human-aligned conversational explanations](https://mlsanity.com/paper/2107.03178/) (2021)
- [Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunities](https://mlsanity.com/paper/2210.11846/) (2022)
- [Reasoning about Counterfactuals to Improve Human Inverse Reinforcement Learning](https://mlsanity.com/paper/2203.01855/) (2022)
- [Policy Optimization with Sparse Global Contrastive Explanations](https://mlsanity.com/paper/2207.06269/) (2022)

## Abstract (arXiv)

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.