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The Roles of Instructional Agents in Human-Agent Interaction Within Serious Games

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HCI International 2022 - Late Breaking Papers. Interaction in New Media, Learning and Games (HCII 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13517))

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Abstract

Automated agent technology in video games is a form of artificial intelligence (AI) that interacts with humans, and thus can provide interactive skills training via human-agent interaction. Having a good framework for agent types could help industry agent designers make best use of agents’ capabilities. This research extends the previously established Human-Agent Team Game Analysis Framework from traditional video games to serious games focused on learning by clarifying the instructional agent types based on characteristics from the literature. Using the characteristics of (1) interaction timing, (2) level of autonomy, and (3) memory, agents of type Planner, Advisor, Critic, Companion, Actor, and Player were analyzed across eight different serious games. The new clarification of agent types could help agent designers decide the best agent type appropriate for their applications.

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References

  1. Laird, J., VanLent, M.: Human-level AI’s killer application: interactive computer games. AI Mag. 22, 15 (2001). https://doi.org/10.1609/aimag.v22i2.1558

    Article  Google Scholar 

  2. Ryu, H., Shin, H., Park, J.: Cooperative and competitive biases for multi-agent reinforcement learning. arXiv (2021). https://doi.org/10.48550/arXiv.2101.06890

  3. Boulanger, C., et al.: Stroke rehabilitation with a sensing surface. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, New York, NY, USA, pp. 1243–1246. Association for Computing Machinery (2013). https://doi.org/10.1145/2470654.2466160

  4. Jianqiang, D.S., Ma, X., Zhao, S., Khoo, J.T., Bay, S.L., Jiang, Z.: Farmer’s tale: a facebook game to promote volunteerism. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Vancouver, BC, Canada, pp. 581–584. ACM (2011). https://doi.org/10.1145/1978942.1979024

  5. Mayer, R.E., Johnson, C.I.: Adding instructional features that promote learning in a game-like environment. J. Educ. Comput. Res. 42, 241–265 (2010). https://doi.org/10.2190/EC.42.3.a

    Article  Google Scholar 

  6. Lelardeux, C.P., Galaup, M., Panzoli, D., Lagarrigue, P., Jessel, J.-P.: A method to design a multi-player educational scenario to make interdisciplinary teams experiment risk management situation in a digital collaborative learning game: a case of study in healthcare. Int. J. Eng. Pedagogy 8, 88–100 (2018)

    Article  Google Scholar 

  7. Bell, B., Sottilare, R.: Adaptation vectors for instructional agents. In: Sottilare, R.A., Schwarz, J. (eds.) HCII 2019. LNCS, vol. 11597, pp. 3–14. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22341-0_1

    Chapter  Google Scholar 

  8. Tutors, C.: Anderson, J.R., Corbett, A.T., Koedinger, K.R., Pelletier, Ray. Lessons Learned. J. Learn. Sci. 4, 167–207 (1995). https://doi.org/10.1207/s15327809jls0402_2

    Article  Google Scholar 

  9. Baylor, A.: Beyond butlers: intelligent agents as mentors. J. Educ. Comput. Res. 22, 373–382 (2000)

    Article  Google Scholar 

  10. Yang, X., Deng, L., Wei, P.: Multi-agent autonomous on-demand free flight operations in urban air mobility. In: AIAA Aviation 2019 Forum. American Institute of Aeronautics and Astronautics, Dallas, Texas (2019). https://doi.org/10.2514/6.2019-3520

  11. Baker, R., et al.: Adapting to when students game an intelligent tutoring system. In: Ikeda, Mitsuru, Ashley, Kevin D., Chan, Tak-Wai. (eds.) ITS 2006. LNCS, vol. 4053, pp. 392–401. Springer, Heidelberg (2006). https://doi.org/10.1007/11774303_39

    Chapter  Google Scholar 

  12. Chiou, E.K., Lee, J.D., Su, T.: Negotiated and reciprocal exchange structures in human-agent cooperation. Comput. Hum. Behav. 90, 288–297 (2019). https://doi.org/10.1016/j.chb.2018.08.012

    Article  Google Scholar 

  13. Gee, J.P.: Good Video Games+ Good Learning: Collected Essays on Video Games, Learning, and Literacy. Peter Lang, New York (2007)

    Book  Google Scholar 

  14. Hainey, T., et al.: Students’ attitudes toward playing games and using games in education: comparing Scotland and the Netherlands. Comput. Educ. 69, 474–484 (2013). https://doi.org/10.1016/j.compedu.2013.07.023

    Article  Google Scholar 

  15. Tüzün, H., Yılmaz-Soylu, M., Karakuş, T., İnal, Y., Kızılkaya, G.: The effects of computer games on primary school students’ achievement and motivation in geography learning. Comput. Educ. 52, 68–77 (2009). https://doi.org/10.1016/j.compedu.2008.06.008

    Article  Google Scholar 

  16. Bickmore, T.W., Pfeifer, L.M., Jack, B.W.: Taking the time to care: empowering low health literacy hospital patients with virtual nurse agents. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, New York, NY, USA, pp. 1265–1274. Association for Computing Machinery (2009). https://doi.org/10.1145/1518701.1518891

  17. Chen, J.Y.C., Barnes, M.J.: Human-agent teaming for multirobot control: a review of human factors issues. IEEE Trans. Hum. Mach. Syst. 44, 13–29 (2014). https://doi.org/10.1109/THMS.2013.2293535

  18. Susi, T., Johannesson, M., Backlund, P.: Serious games: an overview (2007)

    Google Scholar 

  19. Klien, G., Woods, D.D., Bradshaw, J.M., Hoffman, R.R., Feltovich, P.J.: Ten challenges for making automation a “team player” in joint human-agent activity. IEEE Intell. Syst. 19, 91–95 (2004). https://doi.org/10.1109/MIS.2004.74

    Article  Google Scholar 

  20. Neerincx, M.A., van der Waa, J., Kaptein, F., van Diggelen, J.: Using perceptual and cognitive explanations for enhanced human-agent team performance. In: Harris, D. (ed.) EPCE 2018. LNCS (LNAI), vol. 10906, pp. 204–214. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91122-9_18

    Chapter  Google Scholar 

  21. Sepich, N., Dorneich, M.C., Gilbert, S.: Human-agent team game analysis framework: case studies. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 65, pp. 1146–1150 (2021). https://doi.org/10.1177/1071181321651188

  22. Tokadlı, G., Ouverson, K., Meusel, C., Garcia, A., Gilbert, S.B., Dorneich, M.C.: An analysis of video games using the dimensions of human-agent interaction. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, pp. 716–720. SAGE Publications, Los Angeles (2018)

    Google Scholar 

  23. Vagia, M., Transeth, A.A., Fjerdingen, S.A.: A literature review on the levels of automation during the years. What are the different taxonomies that have been proposed? Appl. Ergon. 53, 190–202 (2016). https://doi.org/10.1016/j.apergo.2015.09.013

  24. Leemkuil, H., De Jong, T.O.N.: Adaptive advice in learning with a computer-based knowledge management simulation game. Acad. Manag. Learn. Educ. 11, 653–665 (2012)

    Article  Google Scholar 

  25. Leemkuil, H., De Jong, T., De Hoog, R., Christoph, N.: KM QUEST: a collaborative Internet-based simulation game. Simul. Gaming 34, 89–111 (2003)

    Article  Google Scholar 

  26. Komiak, S., Benbasat, I.: Abstract comparing persuasiveness of different recommendation agents as customer decision support systems in electronic commerce (2004)

    Google Scholar 

  27. Rickel, J., Johnson, W.L.: Animated agents for procedural training in virtual reality: perception, cognition, and motor control. Appl. Artif. Intell. 13, 343–382 (1999)

    Article  Google Scholar 

  28. Chen, Z., Amato, C., Nguyen, T.-H.D., Cooper, S., Sun, Y., El-Nasr, M.S.: Q-DeckRec: a fast deck recommendation system for collectible card games. In: 2018 IEEE Conference on Computational Intelligence and Games (CIG), pp. 1–8. IEEE (2018)

    Google Scholar 

  29. Hanke, L., Chaimowicz, L.: A recommender system for hero line-ups in MOBA games. In: Thirteenth Artificial Intelligence and Interactive Digital Entertainment Conference (2017)

    Google Scholar 

  30. Araujo, V., Rios, F., Parra, D.: Data mining for item recommendation in MOBA games. In: Proceedings of the 13th ACM Conference on Recommender Systems, pp. 393–397 (2019)

    Google Scholar 

  31. Looi, W., Dhaliwal, M., Alhajj, R., Rokne, J.: Recommender system for items in Dota 2. IEEE Trans. Games 11, 396–404 (2018)

    Article  Google Scholar 

  32. Villa, A., Araujo, V., Cattan, F., Parra, D.: Interpretable contextual team-aware item recommendation: application in multiplayer online battle arena games. In: Fourteenth ACM Conference on Recommender Systems, pp. 503–508 (2020)

    Google Scholar 

  33. Cheuque, G., Guzmán, J., Parra, D.: Recommender systems for online video game platforms: the case of steam. In: Companion Proceedings of the 2019 World Wide Web Conference, pp. 763–771 (2019)

    Google Scholar 

  34. Walton, J., Gilbert, S.B.: Evaluating the effect of displaying team vs. individual metrics on team performance. Int. J. Hum. Comput. Stud. 160, 102759 (2022). https://doi.org/10.1016/j.ijhcs.2021.102759

  35. Johnson, C.I., Bailey, S.K.T., Van Buskirk, W.L.: Designing effective feedback messages in serious games and simulations: a research review. In: Wouters, P., van Oostendorp, H. (eds.) Instructional Techniques to Facilitate Learning and Motivation of Serious Games. AGL, pp. 119–140. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-39298-1_7

    Chapter  Google Scholar 

  36. Moreno, R.: Decreasing cognitive load for novice students: effects of explanatory versus corrective feedback in discovery-based multimedia. Instr. Sci. 32, 99–113 (2004)

    Article  Google Scholar 

  37. Moreno, R., Mayer, R.E., Spires, H.A., Lester, J.C.: The case for social agency in computer-based teaching: do students learn more deeply when they interact with animated pedagogical agents? Cogn. Instr. 19, 177–213 (2001). https://doi.org/10.1207/S1532690XCI1902_02

    Article  Google Scholar 

  38. Fiorella, L., Vogel-Walcutt, J.J., Schatz, S.: Applying the modality principle to real-time feedback and the acquisition of higher-order cognitive skills. Educ. Technol. Res. Dev. 60, 223–238 (2012). https://doi.org/10.1007/s11423-011-9218-1

    Article  Google Scholar 

  39. Johnson, C.I., Priest, H.A., Glerum, D.R., Serge, S.R.: Timing of feedback delivery in game-based training. In: Proceedings of the Interservice/Industry Training, Simulation & Education Conference (2013)

    Google Scholar 

  40. Wik, P., Hjalmarsson, A.: Embodied conversational agents in computer assisted language learning. Speech Commun. 51, 1024–1037 (2009)

    Article  Google Scholar 

  41. Nelson, B.C.: Exploring the use of individualized, reflective guidance in an educational multi-user virtual environment. J. Sci. Educ. Technol. 16, 83–97 (2007)

    Article  Google Scholar 

  42. van der Spek, E.D., van Oostendorp, H., Wouters, P., Aarnoudse, L.: Attentional cueing in serious games. In: 2010 Second International Conference on Games and Virtual Worlds for Serious Applications, pp. 119–125 (2010). https://doi.org/10.1109/VS-GAMES.2010.8

  43. Jiang, W., et al.: Social implications of agent-based planning support for human teams. In: 2014 International Conference on Collaboration Technologies and Systems (CTS), pp. 310–317. IEEE (2014)

    Google Scholar 

  44. Goodman, T., Miller, M.E., Rusnock, C.F., Bindewald, J.: Timing within human-agent interaction and its effects on team performance and human behavior. In: 2016 IEEE International Multi-disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), pp. 35–41. IEEE (2016)

    Google Scholar 

  45. McNeese, N.J., Demir, M., Cooke, N.J., Myers, C.: Teaming with a synthetic teammate: insights into human-autonomy teaming. Hum. Factors 60, 262–273 (2018)

    Article  Google Scholar 

  46. Bishop, J., et al.: CHAOPT: a testbed for evaluating human-autonomy team collaboration using the video game overcooked! 2. In: 2020 Systems and Information Engineering Design Symposium (SIEDS), pp. 1–6. IEEE (2020)

    Google Scholar 

  47. Kelley, J.F.: An empirical methodology for writing user-friendly natural language computer applications. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, New York, NY, USA, pp. 193–196. Association for Computing Machinery (1983). https://doi.org/10.1145/800045.801609

  48. Rieth, M., Hagemann, V.: Automation as an equal team player for humans? – A view into the field and implications for research and practice. Appl. Ergon. 98, 103552 (2022). https://doi.org/10.1016/j.apergo.2021.103552

    Article  Google Scholar 

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Acknowledgements

Thanks to Harris Seabold for the creation of the figures in consultation with the authors.

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Authors and Affiliations

  1. Iowa State University, Ames, IA, 50010, USA

    Mohammadamin Sanaei, Stephen B. Gilbert & Michael C. Dorneich

Authors
  1. Mohammadamin Sanaei
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  2. Stephen B. Gilbert
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  3. Michael C. Dorneich
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Corresponding author

Correspondence to Mohammadamin Sanaei .

Editor information

Editors and Affiliations

  1. Computer and Information Sciences, Towson University, Towson, MD, USA

    Gabriele Meiselwitz

  2. San Jose State University, San Jose, CA, USA

    Abbas Moallem

  3. Department of Multimedia and Graphic Arts, Cyprus University of Technology, Limassol, Cyprus

    Panayiotis Zaphiris

  4. Cyprus University of Technology, Limassol, Cyprus

    Andri Ioannou

  5. Soar Technology, Inc., Orlando, FL, USA

    Robert A. Sottilare

  6. MMS, Fraunhofer FKIE, Wachtberg, Nordrhein-Westfalen, Germany

    Jessica Schwarz

  7. DePaul University, Chicago, IL, USA

    Xiaowen Fang

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Sanaei, M., Gilbert, S.B., Dorneich, M.C. (2022). The Roles of Instructional Agents in Human-Agent Interaction Within Serious Games. In: Meiselwitz, G., et al. HCI International 2022 - Late Breaking Papers. Interaction in New Media, Learning and Games. HCII 2022. Lecture Notes in Computer Science, vol 13517. Springer, Cham. https://doi.org/10.1007/978-3-031-22131-6_47

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  • DOI: https://doi.org/10.1007/978-3-031-22131-6_47

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