Summary of Leveraging Counterfactual Paths For Contrastive Explanations Of Pomdp Policies, by Benjamin Kraske et al.
Leveraging Counterfactual Paths for Contrastive Explanations of POMDP Policies
by Benjamin Kraske, Zakariya Laouar, Zachary Sunberg
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed research investigates the application of Explainable Artificial Intelligence (XAI) to partially observable Markov decision processes (POMDPs), with a focus on generating contrastive explanations of POMDP policies. The approach relies on user-provided counterfactuals and feature expectations to provide insights into policy performance. The study is demonstrated in a Search and Rescue (SAR) setting, showcasing the potential for XAI to improve transparency and trust in autonomous systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to make AI systems more understandable by explaining how they work. It uses special decision-making models called POMDPs that can handle uncertainty and provide explanations. The study shows how users can give examples of what would have happened if the system had made different choices, and how this helps understand why a policy was chosen. This is tested in a search and rescue scenario. |
