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The Shoutcasters, the Game Enthusiasts, and the AI: Foraging for Explanations of Real-time Strategy Players

Authors:

Jonathan Dodge,

Andrew Anderson,

Claudia Hilderbrand,

Logan Simpson, and

Margaret BurnettAuthors Info & Claims

ACM Transactions on Interactive Intelligent Systems (TiiS), Volume 11, Issue 1

Article No.: 2, Pages 1 - 46

https://doi.org/10.1145/3396047

Published: 15 March 2021 Publication History

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Abstract

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 article, we present a series of studies¹ 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.

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Cited By

Khanna RDodge JAnderson ADikkala RIrvine JShureih ZLam KMatthews CLin ZKahng MFern ABurnett M(2022)Finding AI’s Faults with AAR/AI: An Empirical StudyACM Transactions on Interactive Intelligent Systems10.1145/348706512:1(1-33)Online publication date: 4-Mar-2022
https://dl.acm.org/doi/10.1145/3487065
Tabatabai DRuangrotsakun AIrvine JDodge JShureih ZLam KBurnett MFern AKahng M(2021)“Why did my AI agent lose?”: Visual Analytics for Scaling Up After-Action Review2021 IEEE Visualization Conference (VIS)10.1109/VIS49827.2021.9623268(16-20)Online publication date: Oct-2021
https://doi.org/10.1109/VIS49827.2021.9623268

Index Terms

The Shoutcasters, the Game Enthusiasts, and the AI: Foraging for Explanations of Real-time Strategy Players
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Intelligent agents
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User studies

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cover image ACM Transactions on Interactive Intelligent Systems

ACM Transactions on Interactive Intelligent Systems Volume 11, Issue 1

March 2021

245 pages

ISSN:2160-6455

EISSN:2160-6463

DOI:10.1145/3453938

Editor:
Michelle X. Zhou
Juji, Inc., USA

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Copyright © 2021 Association for Computing Machinery.

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Publication History

Published: 15 March 2021

Online AM: 07 May 2020

Accepted: 01 April 2020

Revised: 01 February 2020

Received: 01 September 2019

Published in TIIS Volume 11, Issue 1

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Cited By

Khanna RDodge JAnderson ADikkala RIrvine JShureih ZLam KMatthews CLin ZKahng MFern ABurnett M(2022)Finding AI’s Faults with AAR/AI: An Empirical StudyACM Transactions on Interactive Intelligent Systems10.1145/348706512:1(1-33)Online publication date: 4-Mar-2022
https://dl.acm.org/doi/10.1145/3487065
Tabatabai DRuangrotsakun AIrvine JDodge JShureih ZLam KBurnett MFern AKahng M(2021)“Why did my AI agent lose?”: Visual Analytics for Scaling Up After-Action Review2021 IEEE Visualization Conference (VIS)10.1109/VIS49827.2021.9623268(16-20)Online publication date: Oct-2021
https://doi.org/10.1109/VIS49827.2021.9623268

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