Leveraging Counterfactual Paths for Contrastive Explanations of POMDP Policies

• URL: http://arxiv.org/abs/2403.19760v1
• Date: Thu, 28 Mar 2024 18:19:38 GMT
• Title: Leveraging Counterfactual Paths for Contrastive Explanations of POMDP Policies
• Authors: Benjamin Kraske, Zakariya Laouar, Zachary Sunberg,
• Abstract summary: XAI aims to reduce confusion and foster trust in systems by providing explanations of agent behavior. POMDPs provide a flexible framework capable of reasoning over transition and state uncertainty. This work investigates the use of user-provided counterfactuals to generate contrastive explanations of POMDP policies.
• Score: 2.4332936182093197
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As humans come to rely on autonomous systems more, ensuring the transparency of such systems is important to their continued adoption. Explainable Artificial Intelligence (XAI) aims to reduce confusion and foster trust in systems by providing explanations of agent behavior. Partially observable Markov decision processes (POMDPs) provide a flexible framework capable of reasoning over transition and state uncertainty, while also being amenable to explanation. This work investigates the use of user-provided counterfactuals to generate contrastive explanations of POMDP policies. Feature expectations are used as a means of contrasting the performance of these policies. We demonstrate our approach in a Search and Rescue (SAR) setting. We analyze and discuss the associated challenges through two case studies.

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