# Toward Foraging for Understanding of StarCraft Agents: An Empirical Study

arXiv: https://arxiv.org/abs/1711.08019 · HTML: https://mlsanity.com/paper/1711.08019/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2017.

## Built on by (most cited first, 4 of 4)

- [Contrastive Explanations of Plans Through Model Restrictions](https://mlsanity.com/paper/2103.15575/) (2021)
- [How Human-Centered Explainable AI Interface Are Designed and Evaluated: A Systematic Survey](https://mlsanity.com/paper/2403.14496/) (2024)
- [A Survey of Accessible Explainable Artificial Intelligence Research](https://mlsanity.com/paper/2407.17484/) (2024)
- [How to Measure Human-AI Prediction Accuracy in Explainable AI Systems](https://mlsanity.com/paper/2409.00069/) (2024)

## Abstract (arXiv)

Assessing and understanding intelligent agents is a difficult task for users that lack an AI background. A relatively new area, called "Explainable AI," is emerging to help address this problem, but little is known about how users would forage through information an explanation system might offer. To inform the development of Explainable AI systems, we conducted a formative study, using the lens of Information Foraging Theory, into how experienced users foraged in the domain of StarCraft to assess an agent. Our results showed that participants faced difficult foraging problems. These foraging problems caused participants to entirely miss events that were important to them, reluctantly choose to ignore actions they did not want to ignore, and bear high cognitive, navigation, and information costs to access the information they needed.