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Teaming up with Artificial Intelligence: The Human in the Loop of Serious Game Pathfinding Algorithms

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Games and Learning Alliance (GALA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11385))

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Abstract

Serious games’ success depends on its capabilities to engage learners and to provide them with personalized gaming and learning experiences. Therefore, theoretically sound mechanisms for gaining a certain level of understanding of learning and gaming processes by the game is crucial. Consequently, AI and machine learning technologies increasingly enter the field. These technologies often fail, however, since serious games either pose highly complex problems (combining gaming and learning process) or do not provide the extensive data bases that would be required. One solution might be allowing human intelligence or intuition influence AI processes. In the present study, we investigated pathfinding algorithms with and without human interventions to the algorithms. As a testbed, we used a clone of the Travelling Salesman problem, the Travelling Snakesman game. We found some evidence that in this particular pathfinding problem human interventions result in superior results as the MAXMIN Ant System algorithm.

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References

  1. Silver, D., et al.: Mastering the game of Go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016)

    Article  Google Scholar 

  2. Wouters, P.J.M., van Nimwegen, C., van Oostendorp, H., van der Spek, E.D.: A meta-analysis of the cognitive and motivational effects of serious games. J. Educ. Psychol. 105, 249–265 (2013)

    Article  Google Scholar 

  3. Clark, D., Tanner-Smith, E., Killingsworth, S., Bellamy, S.: Digital Games for Learning: A Systematic Review and Meta-Analysis (Executive Summary). SRI International, Menlo Park (2013)

    Google Scholar 

  4. Kickmeier-Rust, M.D.: Balancing on a high wire: adaptivity, a key feature of future learning games. In: Kickmeier-Rust, M.D., Albert, D. (eds.) An Alien’s Guide to Multi-adaptive Educational Games, pp. 43–88. Informing Science Press, Santa Rosa (2012)

    Google Scholar 

  5. Van der Kleij, F.M., Vermeulen, J.A., Schildkamp, K., Eggen, T.J.H.M.: Integrating data-based decision making, assessment for learning and diagnostic testing in formative assessment. Assess. Educ. Princ. Policy Pract. 22(3), 324–343 (2015)

    Google Scholar 

  6. Crisp, G.: Integrative assessment: reframing assessment practice for current and future learning. Assess. Eval. High. Educ. 37(1), 33–43 (2012)

    Article  Google Scholar 

  7. Kickmeier-Rust, M.D., Albert, D.: Educationally adaptive: balancing serious games. Int. J. Comput. Sci. Sport 11(1), 15–28 (2012)

    Google Scholar 

  8. Bellotti, F., Kapralos, B., Lee, L., Moreno-Ger, P., Berta, R.: Assessment in and of serious games: an overview. Adv. Hum. Comput. Interact. 2013, 11 (2013)

    Google Scholar 

  9. Shute, V., Ke, F., Wang, L.: Assessment and adaptation in games. In: Wouters, P., van Oostendorp, H. (eds.) Techniques to Improve the Effectiveness of Serious Games, Advances in Game-Based Learning, pp. 59–78. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39298-1_4

    Chapter  Google Scholar 

  10. D’Mello, S., Graesser, A.C.: Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features. User Model. User-Adap. Inter. 20(2), 147–187 (2010)

    Article  Google Scholar 

  11. Si, M., Marsella, S.C., Pynadath, D.V.: Directorial control in a decision-theoretic framework for interactive narrative. In: International Conference on Interactive Digital Storytelling (ICIDS), pp. 221–233 (2009)

    Chapter  Google Scholar 

  12. Lester, J., Ha, E.Y., Lee, S.Y., Mott, B.W., Rowe, J.P., Sabourin, J.L.: Serious games get smart: intelligent game-based learning environments. AI Mag. 34(4), 31–45 (2013)

    Article  Google Scholar 

  13. Yannakakis, G.N.: Game AI revisited. In: Proceedings of the 9th Conference on Computing Frontiers, pp. 285–292. ACM, May 2012

    Google Scholar 

  14. Cui, X., Shi, H.: A*-based pathfinding in modern computer games. Int. J. Comput. Sci. Network Secur. 11(1), 125–130 (2011)

    Google Scholar 

  15. Silver, D., et al.: Mastering the game of go without human knowledge. Nature 550(7676), 354–359 (2017)

    Article  Google Scholar 

  16. Shute, V.J., Rieber, L., Van Eck, R.: Games . . . and . . . learning. In: Reiser, R., Dempsey, R. (eds.) Trends and Issues in Instructional Design and Technology, 3rd edn., pp. 321–332. Pearson Education Inc., Upper Saddle River (2011)

    Google Scholar 

  17. Frutos-Pascual, M., Zapirain, G.: Review of the use of AI techniques in serious games: decision making and machine learning. IEEE Trans. Comput. Intell. AI Games 9(2) (2015)

    Article  Google Scholar 

  18. Ciolacu, M., Tehrani, A.F., Beer, R.: Education 4.0 — Fostering student’s performance with machine learning methods. In: IEEE 23rd International Symposium for Design and Technology in Electronic Packaging (SIITME) (2017)

    Google Scholar 

  19. Conati, C., Porayska-Pomsta, K., Mavrikis, M.: AI in Education Needs Interpretable Machine Learning: Lessons from Open Learner Modelling. Cornell University Library (2018)

    Google Scholar 

  20. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  21. Bologna, G., Hayashi, Y.: Characterization of symbolic rules embedded in deep dimlp networks: a challenge to transparency of deep learning. J. Artif. Intell. Soft Comput. Res. 7(4), 265–286 (2017)

    Article  Google Scholar 

  22. Amershi, S., Cakmak, M., Knox, W.B., Kulesza, T.: Power to the people: the role of humans in interactive machine learning. AI Mag. 35(4), 105–120 (2014)

    Article  Google Scholar 

  23. Laporte, G.: The traveling salesman problem: an overview of exact and approximate algorithms. Eur. J. Oper. Res. 59(2), 231–247 (1992)

    Article  MathSciNet  Google Scholar 

  24. Karp, R.M.: Mapping the genome: some combinatorial problems arising in molecular biology. In: Proceedings of the Twenty-Fifth Annual ACM Symposium on Theory of Computing (STOC 1993), pp. 278–285 (1993)

    Google Scholar 

  25. Michael, R.G., David, S.J.: Computers and Intractability: A Guide to the Theory of NP-Completeness. Freeman, San Francisco (1979)

    MATH  Google Scholar 

  26. Stützle, T., Hoos, H.H.: Max–min ant system. Future Gener. Comput. Syst. 16(8), 889–914 (2000)

    Article  Google Scholar 

  27. Wertheimer, M.: Productive Thinking, Enlarged edn. Harper & Row, New York (1959)

    Google Scholar 

  28. Holzinger, A.: Interactive Machine Learning for Health Informatics: When do we need the human-in-the-loop? Brain Inform. 3(2), 119–131 (2016)

    Article  Google Scholar 

  29. Holzinger, A.: Human-Computer Interaction and Knowledge Discovery (HCI-KDD): What is the benefit of bringing those two fields to work together? In: Cuzzocrea, A., Kittl, C., Simos, Dimitris E., Weippl, E., Xu, L. (eds.) CD-ARES 2013. LNCS, vol. 8127, pp. 319–328. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40511-2_22

    Chapter  Google Scholar 

  30. Holzinger, K., Mak, K., Kieseberg, P., Holzinger, A.: Can we trust Machine Learning Results? Artificial Intelligence in Safety-Critical decision Support. ERCIM News 112(1), 42–43 (2018)

    Google Scholar 

  31. Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4(2), 100–107 (1968)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute for Educational Assessment, University of Teacher Education, St. Gallen, Switzerland

    Michael D. Kickmeier-Rust

  2. Holzinger Group, HCI-KDD, Institute of Medical Informatics, Statistics and Documentation, Medical University Graz, Graz, Austria

    Andreas Holzinger

Authors
  1. Michael D. Kickmeier-Rust
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  2. Andreas Holzinger
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Corresponding author

Correspondence to Michael D. Kickmeier-Rust .

Editor information

Editors and Affiliations

  1. Institute for Educational Technology - National Research Council of Italy (ITD-CNR), Palermo, Italy

    Manuel Gentile

  2. Institute for Educational Technology - National Research Council of Italy (ITD-CNR), Palermo, Italy

    Mario Allegra

  3. Bauhaus-Universität, Weimar, Germany

    Heinrich Söbke

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Kickmeier-Rust, M.D., Holzinger, A. (2019). Teaming up with Artificial Intelligence: The Human in the Loop of Serious Game Pathfinding Algorithms. In: Gentile, M., Allegra, M., Söbke, H. (eds) Games and Learning Alliance. GALA 2018. Lecture Notes in Computer Science(), vol 11385. Springer, Cham. https://doi.org/10.1007/978-3-030-11548-7_33

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  • DOI: https://doi.org/10.1007/978-3-030-11548-7_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-11547-0

  • Online ISBN: 978-3-030-11548-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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