Human-Agent Team Game Analysis Framework: Case Studies

Abstract

This research details the development of a human-agent team (HAT) analysis framework specifically aimed at video games. The framework identifies different dimensions of interest related to humans and software agents working together. Video games have a variety of user-tested interaction paradigms that may offer useful insights into HAT dynamics, but it can be difficult for researchers to know which games are relevant to their research without a systematic method of characterizing HAT relationships. The framework was developed based on previous literature and gameplay analysis. This paper offers three case studies, applying the framework to the games Madden 21, Call to Arms, and Civilization V. Possible trends related to agent intelligence, team structures, and interdependence are discussed.

Get full access to this article

View all access options for this article.

References

Authors , et al., (2018).

Flathmann

Schelble

Tubre

McNeese

Rodeghero

(2020). Invoking Principles of Groupware to Develop and Evaluate Present and Future Human-Agent Teams. Proceedings of the 8th International Conference on Human-Agent Interaction, 15–24. doi: 10.1145/3406499.3415072

Isern

Moreno

(2016). A Systematic Literature Review of Agents Applied in Healthcare. Journal of Medical Systems, 40(2), 1–14. doi:10.1007/s10916-015-0376-2

Chen

J. Y. C.

Lakhmani

S. G.

Stowers

Selkowitz

A. R.

Wright

J. L.

Barnes

(2018) Situation awareness-based agent transparency and human-autonomy teaming effectiveness. Theoretical Issues in Ergonomics Science, 19:3, 259–282. doi:10.1080/1463922X.2017.1315750

Johnson

Bradshaw

J. M.

Feltovich

P. J.

Jonker

C. M.

Van Riemsdijk

M. B.

Sierhuis

(2014). Coactive design: Designing support for interdependence in joint activity. Journal of Human-Robot Interaction, 3(1), 43–69.

Klien

Woods

D. D.

Bradshaw

J. M.

Hoffman

R. R.

Feltovich

P. J.

(2004). “Ten challenges for making automation a “team player” in joint human-agent activity,” in IEEE Intelligent Systems, 19(6), 91–95. doi:10.1109/MIS.2004.74.

Mercado

J. E.

Rupp

M. A.

Chen

J. Y. C.

Barnes

M. J.

Barber

Procci

(2016). Intelligent Agent Transparency in Human–Agent Teaming for Multi-UxV Management. Human Factors. 58(3), 401–415. doi:10.1177/0018720815621206

Neerincx

M. A.

van der Waa

Kaptein

van Diggelen

(2018, July). Using perceptual and cognitive explanations for enhanced human-agent team performance. International Conference on Engineering Psychology and Cognitive Ergonomics (pp. 204–214). Springer, Cham.

Parasuraman

Sheridan

Wickens

(2000). A model for types and levels of human interaction with automation. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 30(3), 286–297. doi:10.1109/3468.844354.

10.

Riley

(1989). A General Model of Mixed-Initiative Human-Machine Systems. Proceedings of the Human Factors Society Annual Meeting, 33(2), 124–128. doi:10.1177/154193128903300227

11.

Shehadeh

M. A.

Schroeder

Richert

Jeschke

(2017). “Hybrid teams of industry 4.0: A work place considering robots as key players,” IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1208–1213, doi:10.1109/SMC.2017.8122777.

12.

Scholtz

(2003). Theory and evaluation of human robot interactions. Proceedings of the 36th Annual Hawaii International Conference on System Sciences. doi:10.1109/HICSS.2003.1174284