Skip to main content
Springer Nature Link
Log in

“Draw Fast, Guess Slow”: Characterizing Interactions in Cooperative Partially Observable Settings with Online Pictionary as a Case Study

  • Conference paper
  • First Online:
Human-Computer Interaction – INTERACT 2023 (INTERACT 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14144))

Included in the following conference series:

  • 1131 Accesses

Abstract

Cooperative human-human communication becomes challenging when restrictions such as difference in communication modality and limited time are imposed. We use the popular cooperative social game Pictionary as an online multimodal test bed to explore the dynamics of human-human interactions in such settings. As a part of our study, we identify attributes of player interactions that characterize cooperative gameplay. We found stable and role-specific playing style components that are independent of game difficulty. In terms of gameplay and the larger context of cooperative partially observable communication, our results suggest that too much interaction or unbalanced interaction negatively impacts game success. Additionally, the playing style components discovered via our analysis align with select player personality types proposed in existing frameworks for multiplayer games.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Ashktorab, Z., et al.: Effects of communication directionality and AI agent differences in human-AI interaction. In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pp. 1–15 (2021)

    Google Scholar 

  2. Ashktorab, Z., et al.: Human-AI collaboration in a cooperative game setting: measuring social perception and outcomes. Proc. ACM Hum.-Comput. Interact. 4(CSCW2), 1–20 (2020)

    Article  Google Scholar 

  3. Ashton, M., Verbrugge, C.: Measuring cooperative gameplay pacing in world of warcraft. In: Proceedings of the 6th International Conference on Foundations of Digital Games, pp. 77–83 (2011)

    Google Scholar 

  4. Bafadal, M.F., Humaira, H.: The use of charades games in teaching vocabulary to the junior high school students. Linguist. ELT J. 6(2), 14–21 (2019)

    Article  Google Scholar 

  5. Bard, N., et al.: The Hanabi challenge: a new frontier for AI research. Artif. Intell. 280, 103216 (2020)

    Article  MathSciNet  Google Scholar 

  6. Bartle, R.: Players who suit muds. J. MUD Res. 1(1) (1996)

    Google Scholar 

  7. Bauer, A., Popović, Z.: Collaborative problem solving in an open-ended scientific discovery game. Proc. ACM Hum.-Comput. Interact. 1(CSCW), 1–21 (2017)

    Google Scholar 

  8. Beznosyk, A., Quax, P., Coninx, K., Lamotte, W.: The influence of cooperative game design patterns for remote play on player experience. In: Proceedings of the 10th Asia Pacific Conference on Computer Human Interaction, pp. 11–20 (2012)

    Google Scholar 

  9. Borges, S.S., Mizoguchi, R., Durelli, V.H.S., Bittencourt, I.I., Isotani, S.: A link between worlds: towards a conceptual framework for bridging player and learner roles in gamified collaborative learning contexts. In: Koch, F., Koster, A., Primo, T., Guttmann, C. (eds.) CARE/SOCIALEDU -2016. CCIS, vol. 677, pp. 19–34. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-52039-1_2

    Chapter  Google Scholar 

  10. Clark, C., et al.: Iconary: a pictionary-based game for testing multimodal communication with drawings and text. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 1864–1886 (2021)

    Google Scholar 

  11. Colwell, J., Kato, M.: Investigation of the relationship between social isolation, self-esteem, aggression and computer game play in Japanese adolescents. Asian J. Soc. Psychol. 6(2), 149–158 (2003)

    Article  Google Scholar 

  12. Conroy, D., Wyeth, P., Johnson, D.: Modeling player-like behavior for game AI design. In: Proceedings of the 8th International Conference on Advances in Computer Entertainment Technology, pp. 1–8 (2011)

    Google Scholar 

  13. Drachen, A., Sifa, R., Bauckhage, C., Thurau, C.: Guns, swords and data: clustering of player behavior in computer games in the wild. In: 2012 IEEE conference on Computational Intelligence and Games (CIG), pp. 163–170. IEEE (2012)

    Google Scholar 

  14. Eger, M., Martens, C., Córdoba, M.A.: An intentional AI for Hanabi. In: 2017 IEEE Conference on Computational Intelligence and Games (CIG), pp. 68–75. IEEE (2017)

    Google Scholar 

  15. Emmerich, K., Masuch, M.: The impact of game patterns on player experience and social interaction in co-located multiplayer games. In: Proceedings of the Annual Symposium on Computer-Human Interaction in Play, pp. 411–422 (2017)

    Google Scholar 

  16. Fay, N., Walker, B., Swoboda, N.: Deconstructing social interaction: the complimentary roles of behaviour alignment and partner feedback to the creation of shared symbols. In: CogSci (2017)

    Google Scholar 

  17. Ferro, L.S., Walz, S.P., Greuter, S.: Towards personalised, gamified systems: an investigation into game design, personality and player typologies. In: Proceedings of The 9th Australasian Conference on Interactive Entertainment: Matters of Life and Death, pp. 1–6 (2013)

    Google Scholar 

  18. Frans, K.: Ai charades: Language models as interactive game environments. In: 2021 IEEE Conference on Games (CoG), pp. 1–2. IEEE (2021)

    Google Scholar 

  19. Gero, K.I., et al.: Mental models of AI agents in a cooperative game setting. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–12 (2020)

    Google Scholar 

  20. Hooshyar, D., Yousefi, M., Lim, H.: Data-driven approaches to game player modeling: a systematic literature review. ACM Comput. Surv. (CSUR) 50(6), 1–19 (2018)

    Article  Google Scholar 

  21. Hwang, J., Park, S.: An exploratory study of how casino dealer communication styles lead to player satisfaction. J. Travel Tour. Mark. 35(9), 1246–1260 (2018)

    Article  Google Scholar 

  22. Iacovides, I., Cox, A.L., Avakian, A., Knoll, T.: Player strategies: achieving breakthroughs and progressing in single-player and cooperative games. In: Proceedings of the First ACM SIGCHI Annual Symposium on Computer-Human Interaction in Play, pp. 131–140 (2014)

    Google Scholar 

  23. Jiang, J., Maldeniya, D., Lerman, K., Ferrara, E.: The wide, the deep, and the maverick: types of players in team-based online games. Proc. ACM Hum.-Comput. Interact. 5(CSCW1), 1–26 (2021)

    Google Scholar 

  24. Kirman, B., Lawson, S.: Hardcore classification: identifying play styles in social games using network analysis. In: Natkin, S., Dupire, J. (eds.) ICEC 2009. LNCS, vol. 5709, pp. 246–251. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04052-8_28

    Chapter  Google Scholar 

  25. Kou, Y.: Toxic behaviors in team-based competitive gaming: the case of league of legends. In: Proceedings of the Annual Symposium on Computer-Human Interaction in Play, pp. 81–92 (2020)

    Google Scholar 

  26. Lange, A., Somov, A., Stepanov, A., Burnaev, E.: Building a behavioral profile and assessing the skill of video game players. IEEE Sens. J. 22(1), 481–488 (2021)

    Article  Google Scholar 

  27. Li, J., et al.: A difficulty-aware framework for churn prediction and intervention in games. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 943–952 (2021)

    Google Scholar 

  28. Lopez, C.E., Tucker, C.S.: The effects of player type on performance: a gamification case study. Comput. Hum. Behav. 91, 333–345 (2019)

    Article  Google Scholar 

  29. Loria, E., Marconi, A.: Player types and player behaviors: analyzing correlations in an on-the-field gamified system. In: Proceedings of the 2018 Annual Symposium on Computer-Human Interaction in Play Companion Extended Abstracts, pp. 531–538 (2018)

    Google Scholar 

  30. Loria, E., Rivera, J., Marconi, A.: Do they play as intended?-Comparing aggregated and temporal behavioral analysis in a persuasive gamified system. In: HICSS, pp. 1–10 (2021)

    Google Scholar 

  31. Mader, S., Tassin, E.: Playstyles in tetris: beyond player skill, score, and competition. In: Göbl, B., van der Spek, E., Baalsrud Hauge, J., McCall, R. (eds.) ICEC 2022. LNCS, vol. 13477, pp. 162–170. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-20212-4_13

    Chapter  Google Scholar 

  32. Matsumoto, Y., Thawonmas, R.: MMOG player classification using hidden Markov models. In: Rauterberg, M. (ed.) ICEC 2004. LNCS, vol. 3166, pp. 429–434. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-28643-1_55

    Chapter  Google Scholar 

  33. Rahmah, A., Astutik, Y.: Charades game: does it affect students’ learning on English vocabulary. EnJourMe (Engl. J. Merdeka) 5(1), 75–89 (2020)

    Google Scholar 

  34. Ramirez-Cano, D., Colton, S., Baumgarten, R.: Player classification using a meta-clustering approach. In: Proceedings of the 3rd Annual International Conference Computer Games, Multimedia and Allied Technology, pp. 297–304 (2010)

    Google Scholar 

  35. Rodrigues, L.A., Brancher, J.D.: Improving players’ profiles clustering from game data through feature extraction. In: 2018 17th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames), pp. 177–17709. IEEE (2018)

    Google Scholar 

  36. Rogerson, M.J., Gibbs, M.R., Smith, W.: Cooperating to compete: the mutuality of cooperation and competition in boardgame play. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1–13 (2018)

    Google Scholar 

  37. Sarvadevabhatla, R.K., Surya, S., Mittal, T., Babu, R.V.: Pictionary-style word guessing on hand-drawn object sketches: dataset, analysis and deep network models. IEEE Trans. Pattern Anal. Mach. Intell. 42(1), 221–231 (2018)

    Article  Google Scholar 

  38. Seif El-Nasr, M., et al.: Understanding and evaluating cooperative games. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 253–262 (2010)

    Google Scholar 

  39. Spyridonis, F., Daylamani-Zad, D., O’Brien, M.P.: Efficient in-game communication in collaborative online multiplayer games. In: 2018 10th International Conference on Virtual Worlds and Games for Serious Applications (VS-Games), pp. 1–4. IEEE (2018)

    Google Scholar 

  40. Toups, Z.O., Hammer, J., Hamilton, W.A., Jarrah, A., Graves, W., Garretson, O.: A framework for cooperative communication game mechanics from grounded theory. In: Proceedings of the First ACM SIGCHI Annual Symposium on Computer-Human Interaction in Play, pp. 257–266 (2014)

    Google Scholar 

  41. Tuunanen, J., Hamari, J.: Meta-synthesis of player typologies. In: Proceedings of Nordic Digra 2012 Conference: Games in Culture and Society, Tampere, Finland (2012)

    Google Scholar 

  42. Tylkin, P., Radanovic, G., Parkes, D.C.: Learning robust helpful behaviors in two-player cooperative atari environments. In: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems, pp. 1686–1688 (2021)

    Google Scholar 

  43. Wheat, D., Masek, M., Lam, C.P., Hingston, P.: Modeling perceived difficulty in game levels. In: Proceedings of the Australasian Computer Science Week Multiconference, pp. 1–8 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. International Institute of Information Technology, Hyderabad, India

    Kiruthika Kannan, Anandhini Rajendran, Vinoo Alluri & Ravi Kiran Sarvadevabhatla

Authors
  1. Kiruthika Kannan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anandhini Rajendran
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vinoo Alluri
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ravi Kiran Sarvadevabhatla
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kiruthika Kannan .

Editor information

Editors and Affiliations

  1. University of West London, London, UK

    José Abdelnour Nocera

  2. Reykjavik University, Reykjavik, Iceland

    Marta Kristín Lárusdóttir

  3. University of York, York, UK

    Helen Petrie

  4. University of Bari Aldo Moro, Bari, Italy

    Antonio Piccinno

  5. Université Côte d’Azur, Sophia Antipolis Cedex, France

    Marco Winckler

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (mp4 129413 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kannan, K., Rajendran, A., Alluri, V., Sarvadevabhatla, R.K. (2023). “Draw Fast, Guess Slow”: Characterizing Interactions in Cooperative Partially Observable Settings with Online Pictionary as a Case Study. In: Abdelnour Nocera, J., Kristín Lárusdóttir, M., Petrie, H., Piccinno, A., Winckler, M. (eds) Human-Computer Interaction – INTERACT 2023. INTERACT 2023. Lecture Notes in Computer Science, vol 14144. Springer, Cham. https://doi.org/10.1007/978-3-031-42286-7_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-42286-7_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-42285-0

  • Online ISBN: 978-3-031-42286-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships