Summary of Towards Explainable Goal Recognition Using Weight Of Evidence (woe): a Human-centered Approach, by Abeer Alshehri et al.
Towards Explainable Goal Recognition Using Weight of Evidence (WoE): A Human-Centered Approach
by Abeer Alshehri, Amal Abdulrahman, Hajar Alamri, Tim Miller, Mor Vered
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 In this paper, researchers tackle the challenge of goal recognition (GR) by developing an explainable model for GR agents. The traditional approach to GR involves inferring an agent’s unobserved goals from a sequence of observations, but this paper focuses on explaining the GR process in a way that is comprehensible to humans. The authors introduce and evaluate the eXplainable Goal Recognition (XGR) model, which generates explanations for both why and why not questions. They use two human-agent studies to inform their framework for human-centered explanations of GR and develop the XGR model. The paper evaluates the XGR model computationally across eight GR benchmarks and through three user studies. Results show that the XGR model significantly enhances user understanding, trust, and decision-making compared to baseline models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Goal recognition is important in AI because it helps us understand what goals agents are trying to achieve. This can help us work better with those agents or make decisions about how they affect our lives. In this paper, researchers try to make the goal recognition process more understandable by humans. They develop a new model that explains why an agent is doing something and why it might not be doing something else. They test this model on eight different situations where we need to understand what goals agents have. The results show that their model helps people understand things better and makes them more confident in their decisions. |
