# Explainable Goal Recognition: A Framework Based on Weight of Evidence

arXiv: https://arxiv.org/abs/2303.05622 · HTML: https://mlsanity.com/paper/2303.05622/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 51 references that are in the map.
- First posted on arXiv 9 Mar 2023.

## Builds on (most cited first, 15 of 15)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Metrics for Explainable AI: Challenges and Prospects](https://mlsanity.com/paper/1812.04608/) (2018)
- [Explainable Reinforcement Learning Through a Causal Lens](https://mlsanity.com/paper/1905.10958/) (2019)
- [A Human-Grounded Evaluation Benchmark for Local Explanations of Machine Learning](https://mlsanity.com/paper/1801.05075/) (2018)
- [Interpretable Spatio-temporal Attention for Video Action Recognition](https://mlsanity.com/paper/1810.04511/) (2018)
- [CX-ToM: Counterfactual Explanations with Theory-of-Mind for Enhancing Human Trust in Image Recognition Models](https://mlsanity.com/paper/2109.01401/) (2021)
- [GRIT: Fast, Interpretable, and Verifiable Goal Recognition with Learned Decision Trees for Autonomous Driving](https://mlsanity.com/paper/2103.06113/) (2021)
- [From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence](https://mlsanity.com/paper/2104.13299/) (2021)
- [Active Goal Recognition](https://mlsanity.com/paper/1909.11173/) (2019)
- [Towards Explainable AI Planning as a Service](https://mlsanity.com/paper/1908.05059/) (2019)
- [Interpretable Goal Recognition in the Presence of Occluded Factors for Autonomous Vehicles](https://mlsanity.com/paper/2108.02530/) (2021)
- [Heuristic Online Goal Recognition in Continuous Domains](https://mlsanity.com/paper/1709.09839/) (2017)
- [Autonomous Self-Explanation of Behavior for Interactive Reinforcement Learning Agents](https://mlsanity.com/paper/1810.08811/) (2018)
- [An ASP-Based Approach to Counterfactual Explanations for Classification](https://mlsanity.com/paper/2004.13237/) (2020)
- [Verifiable Goal Recognition for Autonomous Driving with Occlusions](https://mlsanity.com/paper/2206.14163/) (2022)

## Built on by (most cited first, 3 of 3)

- [ADESSE: Advice Explanations in Complex Repeated Decision-Making Environments](https://mlsanity.com/paper/2405.20705/) (2024)
- [Human Goal Recognition as Bayesian Inference: Investigating the Impact of Actions, Timing, and Goal Solvability](https://mlsanity.com/paper/2402.10510/) (2024)
- [Assessing Alignment and Stability of Feature Importance Explanations via Weight of Evidence](https://mlsanity.com/paper/2609.00090/) (2026)

## Abstract (arXiv)

We introduce and evaluate an eXplainable Goal Recognition (XGR) model that uses the Weight of Evidence (WoE) framework to explain goal recognition problems. Our model provides human-centered explanations that answer why? and why not? questions. We computationally evaluate the performance of our system over eight different domains. Using a human behavioral study to obtain the ground truth from human annotators, we further show that the XGR model can successfully generate human-like explanations. We then report on a study with 60 participants who observe agents playing Sokoban game and then receive explanations of the goal recognition output. We investigate participants' understanding obtained by explanations through task prediction, explanation satisfaction, and trust.