TY - GEN
T1 - Toward foraging for understanding of StarCraft agents
T2 - 23rd ACM International Conference on Intelligent User Interfaces, IUI 2018
AU - Penney, Sean
AU - Dodge, Jonathan
AU - Hilderbrand, Claudia
AU - Anderson, Andrew
AU - Simpson, Logan
AU - Burnett, Margaret
N1 - Funding Information:
This work was supported by DARPA #N66001-17-2-4030 and NSF #1314384. Any opinions, findings and conclusions or recommendations expressed are those of the authors and do not necessarily reflect the views of NSF, DARPA, the Army Research Office, or the US government.
Funding Information:
This work was supported by DARPA #N66001-17-2-4030 and NSF # 1314384. Any opinions, findings and conclusions or recommendations expressed are those of the authors and do not necessarily reflect the views of NSF, DARPA, the Army Research Office, or the US government.
Publisher Copyright:
Copyright © 2018 ACM.
PY - 2018/3/5
Y1 - 2018/3/5
N2 - 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, reluctantiy choose to ignore actions they did not want to ignore, and bear high cognitive, navigation, and information costs to access the information they needed.
AB - 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, reluctantiy choose to ignore actions they did not want to ignore, and bear high cognitive, navigation, and information costs to access the information they needed.
UR - http://www.scopus.com/inward/record.url?scp=85044516678&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044516678&partnerID=8YFLogxK
U2 - 10.1145/3172944.3172946
DO - 10.1145/3172944.3172946
M3 - Conference contribution
AN - SCOPUS:85044516678
T3 - International Conference on Intelligent User Interfaces, Proceedings IUI
SP - 225
EP - 237
BT - IUI 2018 - Proceedings of the 23rd International Conference on Intelligent User Interfaces
PB - Association for Computing Machinery
Y2 - 7 March 2018 through 11 March 2018
ER -