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
Silver, D., et al.: Mastering the game of Go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016)
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)
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)
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)
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)
Crisp, G.: Integrative assessment: reframing assessment practice for current and future learning. Assess. Eval. High. Educ. 37(1), 33–43 (2012)
Kickmeier-Rust, M.D., Albert, D.: Educationally adaptive: balancing serious games. Int. J. Comput. Sci. Sport 11(1), 15–28 (2012)
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)
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
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)
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)
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)
Yannakakis, G.N.: Game AI revisited. In: Proceedings of the 9th Conference on Computing Frontiers, pp. 285–292. ACM, May 2012
Cui, X., Shi, H.: A*-based pathfinding in modern computer games. Int. J. Comput. Sci. Network Secur. 11(1), 125–130 (2011)
Silver, D., et al.: Mastering the game of go without human knowledge. Nature 550(7676), 354–359 (2017)
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)
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)
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)
Conati, C., Porayska-Pomsta, K., Mavrikis, M.: AI in Education Needs Interpretable Machine Learning: Lessons from Open Learner Modelling. Cornell University Library (2018)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
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)
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)
Laporte, G.: The traveling salesman problem: an overview of exact and approximate algorithms. Eur. J. Oper. Res. 59(2), 231–247 (1992)
Karp, R.M.: Map** 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)
Michael, R.G., David, S.J.: Computers and Intractability: A Guide to the Theory of NP-Completeness. Freeman, San Francisco (1979)
Stützle, T., Hoos, H.H.: Max–min ant system. Future Gener. Comput. Syst. 16(8), 889–914 (2000)
Wertheimer, M.: Productive Thinking, Enlarged edn. Harper & Row, New York (1959)
Holzinger, A.: Interactive Machine Learning for Health Informatics: When do we need the human-in-the-loop? Brain Inform. 3(2), 119–131 (2016)
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
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)
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)
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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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