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Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios
conference contribution
posted on 2023-02-22, 03:36 authored by F Cruz, C Young, Richard DazeleyRichard Dazeley, P VamplewExplainable artificial intelligence is a research field that tries to provide more transparency for autonomous intelligent systems. Explainability has been used, particularly in reinforcement learning and robotic scenarios, to better understand the robot decision-making process. Previous work, however, has been widely focused on providing technical explanations that can be better understood by AI practitioners than non-expert end-users. In this work, we make use of human-like explanations built from the probability of success to complete the goal that an autonomous robot shows after performing an action. These explanations are intended to be understood by people who have no or very little experience with artificial intelligence methods. This paper presents a user trial to study whether these explanations that focus on the probability an action has of succeeding in its goal constitute a suitable explanation for non-expert end-users. The results obtained show that non-expert participants rate robot explanations that focus on the probability of success higher and with less variance than technical explanations generated from Q-values, and also favor counterfactual explanations over standalone explanations.
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Volume
2022-OctoberPagination
894-901Publisher DOI
Start date
2022-10-23End date
2022-10-27ISSN
2153-0858eISSN
2153-0866ISBN-13
9781665479271Title of proceedings
IEEE International Conference on Intelligent Robots and SystemsEvent
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)Publisher
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