Toward Foraging for Understanding of StarCraft Agents: An Empirical Study
This addresses the challenge for non-expert users in evaluating AI agents, but it is incremental as it builds on existing Explainable AI concepts with a specific empirical focus.
The study tackled the problem of users lacking AI background struggling to assess intelligent agents by investigating how experienced users forage for information to understand a StarCraft agent, using Information Foraging Theory, and found that participants faced difficult foraging problems leading them to miss important events, ignore unwanted actions, and bear high costs.
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.