Résumé: Assessing and understanding intelligent agents is a difficult task for users who lack an AI background. ?Explainable AI? (XAI) aims to address this problem, but what should be in an explanation? One route toward answering this question is to turn to theories of how humans try to obtain information they seek. Information Foraging Theory (IFT) is one such theory. In this paper, we present a series of studies using IFT: the first investigates how expert explainers supply explanations in the RTS domain, the second investigates what explanations domain experts demand from agents in the RTS domain, and the last focuses on how both populations try to explain a state-of-the-art AI. Our results show that RTS environments like StarCraft offer so many options that change so rapidly, foraging tends to be very costly. Ways foragers attempted to manage such costs included ?satisficing? approaches to reduce their cognitive load, such as focusing more on What information than on Why information, strategic use of language to communicate a lot of nuanced information in a few words, and optimizing their environment when possible to make their most valuable information patches readily available. Further, when a real AI entered the picture, even very experienced domain experts had difficulty understanding and judging some of the AI's unconventional behaviors. Finally, our results reveal ways Information Foraging Theory can inform future XAI interactive explanation environments, and also how XAI can inform IFT.

Langue: Anglais