Explainable 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.

本文探讨了透明度逐渐成为自主智能系统的必要品质，提供了人性化的基于成功概率的解释方法，并通过用户调查证明该种解释对于非专业人士更易理解和接受。