research-article

Public Access

How the Experts Do It: Assessing and Explaining Agent Behaviors in Real-Time Strategy Games

Authors:

Jonathan Dodge,

Claudia Hilderbrand,

Andrew Anderson,

Margaret BurnettAuthors Info & Claims

CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

Paper No.: 562, Pages 1 - 12

https://doi.org/10.1145/3173574.3174136

Published: 21 April 2018 Publication History

Abstract

How should an AI-based explanation system explain an agent's complex behavior to ordinary end users who have no background in AI? Answering this question is an active research area, for if an AI-based explanation system could effectively explain intelligent agents' behavior, it could enable the end users to understand, assess, and appropriately trust (or distrust) the agents attempting to help them. To provide insights into this question, we turned to human expert explainers in the real-time strategy domain --"shoutcasters"-- to understand (1) how they foraged in an evolving strategy game in real time, (2) how they assessed the players' behaviors, and (3) how they constructed pertinent and timely explanations out of their insights and delivered them to their audience. The results provided insights into shoutcasters' foraging strategies for gleaning information necessary to assess and explain the players; a characterization of the types of implicit questions shoutcasters answered; and implications for creating explanations by using the patterns and abstraction levels these human experts revealed.

Supplementary Material

ZIP File (pn4411-file4.zip)

Download
1.65 MB

MP4 File (pn4411.mp4)

Download
239.32 MB

References

[1]

Adrian K Agogino and Kagan Tumer. 2004. Unifying temporal and structural credit assignment problems. In Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems-Volume 2. IEEE Computer Society, 980--987.

Digital Library

[2]

Svetlin Bostandjiev, John O'Donovan, and Tobias Höllerer. 2012. TasteWeights: A visual interactive hybrid recommender system. In Proceedings of the Sixth ACM Conference on Recommender Systems. ACM, 35--42.

Digital Library

[3]

Nico Castelli, Corinna Ogonowski, Timo Jakobi, Martin Stein, Gunnar Stevens, and Volker Wulf. 2017. What happened in my home?: An end-user development approach for smart home data visualization. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, 853--866.

Digital Library

[4]

Gifford Cheung and Jeff Huang. 2011. Starcraft from the stands: Understanding the game spectator. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '11). ACM, New York, NY, USA, 763--772.

Digital Library

[5]

Ed H Chi, Peter Pirolli, Kim Chen, and James Pitkow. 2001. Using information scent to model user information needs and actions and the web. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 490--497.

Digital Library

[6]

Kelley Cotter, Janghee Cho, and Emilee Rader. 2017. Explaining the news feed algorithm: An analysis of the "News Feed FYI" blog. In Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems. ACM, 1553--1560.

Digital Library

[7]

Jonathan Dodge et al. 2018. Supplemental materials: How the experts do it: Assessing and explaining agent behaviors in real-time strategy games. web site. (2018). Retrieved December 28, 2017 from http://web.engr.oregonstate.edu/~burnett/ XAI-CHI2018-rebuilt_supplementary_materials/.

[8]

Scott D. Fleming, Chris Scaffidi, David Piorkowski, Margaret Burnett, Rachel Bellamy, Joseph Lawrance, and Irwin Kwan. 2013. An information foraging theory perspective on tools for debugging, refactoring, and reuse tasks. ACM Transactions on Software Engineering and Methodology (TOSEM) 22, 2 (2013), 14.

Digital Library

[9]

Wai-Tat Fu and Peter Pirolli. 2007. SNIF-ACT: A cognitive model of user navigation on the world wide web. Human-Computer Interaction 22, 4 (2007), 355--412.

Digital Library

[10]

Alex Groce, Todd Kulesza, Chaoqiang Zhang, Shalini Shamasunder, Margaret Burnett, Weng-Keen Wong, Simone Stumpf, Shubhomoy Das, Amber Shinsel, Forrest Bice, and others. 2014. You are the only possible oracle: Effective test selection for end users of interactive machine learning systems. IEEE Transactions on Software Engineering 40, 3 (2014), 307--323.

Digital Library

[11]

Robert R Hoffman and Gary Klein. 2017. Explaining explanation, part 1: theoretical foundations. IEEE Intelligent Systems 32, 3 (2017), 68--73.

Digital Library

[12]

Ashish Kapoor, Bongshin Lee, Desney Tan, and Eric Horvitz. 2010. Interactive optimization for steering machine classification. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 1343--1352.

Digital Library

[13]

Man-Je Kim, Kyung-Joong Kim, SeungJun Kim, and Anind K Dey. 2016. Evaluation of starcraft artificial intelligence competition bots by experienced human players. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems. ACM, 1915--1921.

Digital Library

[14]

Josua Krause, Adam Perer, and Kenney Ng. 2016. Interacting with predictions: Visual inspection of black-box machine learning models. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI '16). ACM, New York, NY, USA, 5686--5697.

Digital Library

[15]

Cliff Kuang. 2017. Can AI be taught to explain itself? New York Times, (2017). Retrieved December 26, 2017 from https://www.nytimes.com/2017/11/21/magazine/can-ai-be-taught-to-explain-itself.html.

[16]

Todd Kulesza, Margaret Burnett, Weng-Keen Wong, and Simone Stumpf. 2015. Principles of explanatory debugging to personalize interactive machine learning. In Proceedings of the 20th International Conference on Intelligent User Interfaces. ACM, 126--137.

Digital Library

[17]

Todd Kulesza, Simone Stumpf, Margaret Burnett, and Irwin Kwan. 2012. Tell me more? The effects of mental model soundness on personalizing an intelligent agent. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 1--10.

Digital Library

[18]

Sandeep Kaur Kuttal, Anita Sarma, and Gregg Rothermel. 2013. Predator behavior in the wild web world of bugs: An information foraging theory perspective. In Visual Languages and Human-Centric Computing (VL/HCC), 2013 IEEE Symposium on. IEEE, 59--66.

[19]

Brian Y Lim and Anind K Dey. 2009. Assessing demand for intelligibility in context-aware applications. In Proceedings of the 11th international conference on Ubiquitous computing. ACM, 195--204.

Digital Library

[20]

Brian Y. Lim, Anind K. Dey, and Daniel Avrahami. 2009. Why and why not explanations improve the intelligibility of context-aware intelligent systems. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 2119--2128.

Digital Library

[21]

Diane Litman, Steve Young, M.J.F. Gales, Kate Knill, Karen Ottewell, Rogier van Dalen, and David Vandyke. 2016. Towards using conversations with spoken dialogue systems in the automated assessment of non-native speakers of English. In SIGDIAL Conference. 270--275.

[22]

Ronald Metoyer, Simone Stumpf, Christoph Neumann, Jonathan Dodge, Jill Cao, and Aaron Schnabel. 2010. Explaining how to play real-time strategy games. Knowledge-Based Systems 23, 4 (2010), 295--301.

Digital Library

[23]

Nan Niu, Anas Mahmoud, Zhangji Chen, and Gary Bradshaw. 2013. Departures from optimality: Understanding human analyst's information foraging in assisted requirements tracing. In Proceedings of the 2013 International Conference on Software Engineering. IEEE Press, 572--581.

Digital Library

[24]

Donald A Norman. 1983. Some observations on mental models. Mental models 7, 112 (1983), 7--14.

[25]

S. Ontañón, G. Synnaeve, A. Uriarte, F. Richoux, D. Churchill, and M. Preuss. 2013. A survey of real-time strategy game AI research and competition in StarCraft. IEEE Transactions on Computational Intelligence and AI in Games 5, 4 (Dec 2013), 293--311.

[26]

Alexandre Perez and Rui Abreu. 2014. A diagnosis-based approach to software comprehension. In Proceedings of the 22nd International Conference on Program Comprehension. ACM, 37--47.

Digital Library

[27]

David Piorkowski, Scott D. Fleming, Christopher Scaffidi, Margaret Burnett, Irwin Kwan, Austin Z Henley, Jamie Macbeth, Charles Hill, and Amber Horvath. 2015. To fix or to learn? How production bias affects developers' information foraging during debugging. In Software Maintenance and Evolution (ICSME), 2015 IEEE International Conference on. IEEE, 11--20.

Digital Library

[28]

David Piorkowski, Austin Z Henley, Tahmid Nabi, Scott D Fleming, Christopher Scaffidi, and Margaret Burnett. 2016. Foraging and navigations, fundamentally: Developers' predictions of value and cost. In Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering. ACM, 97--108.

Digital Library

[29]

Peter Pirolli. 2007. Information foraging theory: Adaptive interaction with information. Oxford University Press.

Digital Library

[30]

Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. Why should I trust you?: Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1135--1144.

Digital Library

[31]

Stuart J. Russell and Peter Norvig. 2003. Artificial Intelligence: A modern approach (2 ed.). Pearson Education.

Digital Library

[32]

Robert Spence. 2007. Information Visualization: Design for interaction (2Nd Edition). Prentice-Hall, Inc., Upper Saddle River, NJ, USA.

Digital Library

[33]

Sruti Srinivasa Ragavan, Sandeep Kaur Kuttal, Charles Hill, Anita Sarma, David Piorkowski, and Margaret Burnett. 2016. Foraging among an overabundance of similar variants. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 3509--3521.

Digital Library

[34]

David J Stracuzzi, Alan Fern, Kamal Ali, Robin Hess, Jervis Pinto, Nan Li, Tolga Konik, and Daniel G Shapiro. 2011. An application of transfer to american football: From observation of raw video to control in a simulated environment. AI Magazine 32, 2 (2011), 107--125.

[35]

Adam Summerville, Michael Cook, and Ben Steenhuisen. 2016. Draft-Analysis of the Ancients: Predicting Draft Picks in DotA 2 using Machine Learning. (2016). https://aaai.org/ocs/index.php/AIIDE/AIIDE16/paper/ view/14075

[36]

Katia Sycara, Christian Lebiere, Yulong Pei, Donald Morrison, and Michael Lewis. 2015. Abstraction of analytical models from cognitive models of human control of robotic swarms. In International Conference on Cognitive Modeling. University of Pittsburgh.

[37]

Joe Tullio, Anind K Dey, Jason Chalecki, and James Fogarty. 2007. How it works: A field study of non-technical users interacting with an intelligent system. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 31--40.

Digital Library

[38]

Oriol Vinyals et al. 2017. StarCraft II: A New Challenge for Reinforcement Learning. Tech Report. (2017). Retrieved December 22, 2017 from https://deepmind.com/documents/110/sc2le.pdf.

[39]

Robert H Wortham, Andreas Theodorou, and Joanna J Bryson. 2017. Improving robot transparency:real-time visualisation of robot AI substantially improves understanding in naive observers, In IEEE RO-MAN 2017. IEEE RO-MAN 2017 (August 2017). http://opus.bath.ac.uk/55793/

Cited By

Vered MLivni THowe PMiller TSonenberg L(2023)The Effects of Explanations on Automation BiasArtificial Intelligence10.1016/j.artint.2023.103952(103952)Online publication date: Jun-2023
https://doi.org/10.1016/j.artint.2023.103952
Brasse JBroder HFörster MKlier MSigler I(2023)Explainable artificial intelligence in information systems: A review of the status quo and future research directionsElectronic Markets10.1007/s12525-023-00644-533:1Online publication date: 27-May-2023
https://doi.org/10.1007/s12525-023-00644-5
Degen HBudnik CGross RRothering M(2023)How to Explain It to a Model Manager?Artificial Intelligence in HCI10.1007/978-3-031-35891-3_14(209-242)Online publication date: 9-Jul-2023
https://doi.org/10.1007/978-3-031-35891-3_14
Show More Cited By

Index Terms

How the Experts Do It: Assessing and Explaining Agent Behaviors in Real-Time Strategy Games
1. Computing methodologies
  1. Artificial intelligence
2. Human-centered computing
  1. Human computer interaction (HCI)

Recommendations

Toward Foraging for Understanding of StarCraft Agents: An Empirical Study
IUI '18: Proceedings of the 23rd International Conference on Intelligent User Interfaces

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 ...
The Shoutcasters, the Game Enthusiasts, and the AI: Foraging for Explanations of Real-time Strategy Players
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 ...
Towards human-like artificial intelligence using StarCraft 2
FDG '18: Proceedings of the 13th International Conference on the Foundations of Digital Games

On our path towards artificial general intelligence, video games have become excellent tools for research. Reinforcement learning (RL) algorithms are particularly successful in this domain, with the added benefit of having fairly well established ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

April 2018

8489 pages

ISBN:9781450356206

DOI:10.1145/3173574

General Chairs:
Regan Mandryk
University of Saskatchewan, Canada
,
Mark Hancock
University of Waterloo, Canada
,
Program Chairs:
Mark Perry
Brunel University London, UK
,
Anna Cox
University College London, UK

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 April 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

NSF
DARPA

Conference

CHI '18

Sponsor:

SIGCHI

CHI '18: CHI Conference on Human Factors in Computing Systems

April 21 - 26, 2018

Montreal QC, Canada

Acceptance Rates

CHI '18 Paper Acceptance Rate 666 of 2,590 submissions, 26%;

Overall Acceptance Rate 5,713 of 24,194 submissions, 24%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

20
Total Citations
View Citations
1,075
Total Downloads

Downloads (Last 12 months)188
Downloads (Last 6 weeks)21

Reflects downloads up to 18 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Vered MLivni THowe PMiller TSonenberg L(2023)The Effects of Explanations on Automation BiasArtificial Intelligence10.1016/j.artint.2023.103952(103952)Online publication date: Jun-2023
https://doi.org/10.1016/j.artint.2023.103952
Brasse JBroder HFörster MKlier MSigler I(2023)Explainable artificial intelligence in information systems: A review of the status quo and future research directionsElectronic Markets10.1007/s12525-023-00644-533:1Online publication date: 27-May-2023
https://doi.org/10.1007/s12525-023-00644-5
Degen HBudnik CGross RRothering M(2023)How to Explain It to a Model Manager?Artificial Intelligence in HCI10.1007/978-3-031-35891-3_14(209-242)Online publication date: 9-Jul-2023
https://doi.org/10.1007/978-3-031-35891-3_14
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
Dodge JKhanna RIrvine JLam KMai TLin ZKiddle NNewman EAnderson ARaja SMatthews CPerdriau CBurnett MFern A(2021)After-Action Review for AI (AAR/AI)ACM Transactions on Interactive Intelligent Systems10.1145/345317311:3-4(1-35)Online publication date: 3-Sep-2021
https://dl.acm.org/doi/10.1145/3453173
Shen YWijayaratne NDu PJiang SDriggs-Campbell K(2021)AutoPreview: A Framework for Autopilot Behavior UnderstandingExtended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411763.3451591(1-6)Online publication date: 8-May-2021
https://dl.acm.org/doi/10.1145/3411763.3451591
Robertson JKokkinakis AHook JKirman BBlock FUrsu MPatra SDemediuk SDrachen AOlarewaju OHammond TVerbert KParra DKnijnenburg BO'Donovan JTeale P(2021)Wait, But Why?: Assessing Behavior Explanation Strategies for Real-Time Strategy GamesProceedings of the 26th International Conference on Intelligent User Interfaces10.1145/3397481.3450699(32-42)Online publication date: 14-Apr-2021
https://dl.acm.org/doi/10.1145/3397481.3450699
Penney SDodge JAnderson AHilderbrand CSimpson LBurnett M(2021)The Shoutcasters, the Game Enthusiasts, and the AI: Foraging for Explanations of Real-time Strategy PlayersACM Transactions on Interactive Intelligent Systems10.1145/339604711:1(1-46)Online publication date: 15-Mar-2021
https://dl.acm.org/doi/10.1145/3396047
Averkin AYarushev S(2021)Review of Research in the Field of Developing Methods to Extract Rules From Artificial Neural NetworksJournal of Computer and Systems Sciences International10.1134/S106423072106004660:6(966-980)Online publication date: 17-Dec-2021
https://doi.org/10.1134/S1064230721060046
Chromik MButz A(2021)Human-XAI Interaction: A Review and Design Principles for Explanation User InterfacesHuman-Computer Interaction – INTERACT 202110.1007/978-3-030-85616-8_36(619-640)Online publication date: 26-Aug-2021
https://doi.org/10.1007/978-3-030-85616-8_36
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents