Tag Archives: Explainability

POMDPs focused on obtaining policies that can be understood well just through the observation of the robot actions

Miguel Faria, Francisco S. Melo, Ana Paiva, “Guess what I’m doing”: Extending legibility to sequential decision tasks, Artificial Intelligence, Volume 330, 2024 DOI: 10.1016/j.artint.2024.104107.

In this paper we investigate the notion of legibility in sequential decision tasks under uncertainty. Previous works that extend legibility to scenarios beyond robot motion either focus on deterministic settings or are computationally too expensive. Our proposed approach, dubbed PoLMDP, is able to handle uncertainty while remaining computationally tractable. We establish the advantages of our approach against state-of-the-art approaches in several scenarios of varying complexity. We also showcase the use of our legible policies as demonstrations in machine teaching scenarios, establishing their superiority in teaching new behaviours against the commonly used demonstrations based on the optimal policy. Finally, we assess the legibility of our computed policies through a user study, where people are asked to infer the goal of a mobile robot following a legible policy by observing its actions.

Extracting video summaries from RL processes to explain and understand them

Pedro Sequeira, Melinda Gervasio, Interestingness elements for explainable reinforcement learning: Understanding agents’ capabilities and limitations. Artificial Intelligence, Volume 288, 2020 DOI: 10.1016/j.artint.2020.103367.

We propose an explainable reinforcement learning (XRL) framework that analyzes an agent’s history of interaction with the environment to extract interestingness elements that help explain its behavior. The framework relies on data readily available from standard RL algorithms, augmented with data that can easily be collected by the agent while learning. We describe how to create visual summaries of an agent’s behavior in the form of short video-clips highlighting key interaction moments, based on the proposed elements. We also report on a user study where we evaluated the ability of humans to correctly perceive the aptitude of agents with different characteristics, including their capabilities and limitations, given visual summaries automatically generated by our framework. The results show that the diversity of aspects captured by the different interestingness elements is crucial to help humans correctly understand an agent’s strengths and limitations in performing a task, and determine when it might need adjustments to improve its performance.