Abstract
This paper reports about teaching Artificial Intelligence (AI) by applying the experiential approach called “learning by doing”, where traditional, formal teaching is integrated with a practical activity (a game competition, in our case), that is relevant for AI discipline and allows for an active and playful participation of students.
Students of the course of Fundamentals of AI at the University of Bologna have been challenged (on a voluntary base) to develop an AI software able to play the game of Nine Men’s Morris: at the end of the course, the software players have been compared within a tournament, so as to establish the competition winner. The game has been chosen to let the students deepen the knowledge about AI techniques in solving games, and to apply it in a real, not trivial setting.
The significance and the impact of this approach, from the educational point of view, have been assessed through two questionnaires, a first one focused on the technical aspects, and a second one on the students’ opinions about the initiative. The results are encouraging: students declare they felt highly motivated in studying AI algorithms and techniques, and they have been stimulated in autonomously search for extensions and new solutions not deeply investigated during traditional lessons.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
- 2.
- 3.
- 4.
- 5.
Students are allowed to repeat the test, even in case of sufficient marks.
References
Allis, L.V., et al.: Searching for Solutions in Games and Artificial Intelligence. Ponsen & Looijen, Wageningen (1994)
Bell, R.C.: Board and Table Games from Many Civilizations, vol. 1. Courier Corporation, New York (1979)
Carpio Caada, J., Mateo Sanguino, T., Merelo Guervs, J., Rivas Santos, V.: Open classroom: enhancing student achievement on artificial intelligence through an international online competition. J. Comput. Assist. Learn. 31(1), 14–31 (2015). http://dx.doi.org/10.1111/jcal.12075
Dewey, J.: How We Think. DC Heath, Lexington (1910)
Galassi, A.: Symbolic versus sub-symbolic approaches: a case study on training Deep Networks to play Nine Men’s Morris game. Master’s thesis, University of Bologna, Italy. http://amslaurea.unibo.it/12859/
Gasser, R.: Solving Nine Men’s Morris. Comput. Intell. 12(1), 24–41 (1996). http://dx.doi.org/10.1111/j.1467-8640.1996.tb00251.x
Gévay, G.E., Danner, G.: Calculating ultrastrong and extended solutions for Nine Men’s Morris, Morabaraba, and Lasker Morris. IEEE Trans. Comput. Intell. AI Games 8(3), 256–267 (2016)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016). http://www.deeplearningbook.org
Kaye, A.: Learning together apart. In: Kaye, A.R. (ed.) Collaborative Learning Through Computer Conferencing, vol. 90, pp. 1–24. Springer, Heidelberg (1992)
Lave, J., Wenger, E.: Situated Learning: Legitimate Peripheral Participation. Cambridge University Press, Cambridge (1991)
Petcu, S.A., Holban, S.: Nine Men’s Morris: evaluation functions. In: International Conferencs on Development and Application Systems, pp. 89–92 (2008)
Pumilia-Gnarini, P.M.: Handbook of Research on Didactic Strategies and Technologies for Education: Incorporating Advancements: Incorporating Advancements. IGI Global, Hershey (2012)
Reinefeld, A.: Spielbaum-Suchverfahren, vol. 200. Springer, Heidelberg (2013)
Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice-Hall, Inc., Upper Saddle River (2010)
Rutko, D.: Fuzzified algorithm for game tree search. In: Second Brazilian Workshop of the Game Theory Society, BWGT (2010)
Stanley, K.O., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evol. Comput. 10(2), 99–127 (2002). http://dx.doi.org/10.1162/106365602320169811
Togelius, J.: How to run a successful game-based AI competition. IEEE Trans. Comput. Intell. AI Games 8(1), 95–100 (2016)
Vygotskij, L.S.: Myslenie i rec Psichologiceskie issledovanija. Movska-Leningrad: Gosudarstvennoe Socialno-Ekonomiceskoe Izdatelstvo (Bari, Laterza, It. tr. Pensiero e linguaggio 1992) (1934)
Acknowledgements
We would like to thank all the students who have participated to the competition, and whose names are available on the competition website. Moreover, we are especially grateful to those students who answered our questionnaires. We would like to thank also the reviewers for their helpful and detailed suggestions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Chesani, F., Galassi, A., Mello, P., Trisolini, G. (2017). A Game-Based Competition as Instrument for Teaching Artificial Intelligence. In: Esposito, F., Basili, R., Ferilli, S., Lisi, F. (eds) AI*IA 2017 Advances in Artificial Intelligence. AI*IA 2017. Lecture Notes in Computer Science(), vol 10640. Springer, Cham. https://doi.org/10.1007/978-3-319-70169-1_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-70169-1_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70168-4
Online ISBN: 978-3-319-70169-1
eBook Packages: Computer ScienceComputer Science (R0)