Player Profiling using Hidden Markov Models Supported with the Sliding Window Method
Alper Kilic, Mehmet Akif Gunes, Sanem Sariel
2016
Abstract
In this paper, we present a player profiling system applicable for both human players and bots in video games. The Vindinium artificial intelligence (AI) contest is selected as the test-bed for analyzing the performance of our system. In this game, AI bots compete with each other in a systematically generated environment to achieve the highest score. Our profiling method is based on Hidden Markov Model (HMM) constructed by using consecutive actions of AI bots and improved with the initial training phase and our sliding window approach. The method is evaluated for three different performance criteria: recognition of bots, grouping bots that have similar game styles and tracking changes in the strategy of a single bot through the game. The results indicate that the method is promising with 90,04% binary classification success in average.
References
- Brent Harrison, David L. Roberts, (2011). Using sequential observations to model and predict player behavior. Proceedings of the 6th International Conference on Foundations of Digital Games, p.91-98, Bordeaux, France.
- D. Kennerly, (2003). Better game design through data mining. Gamasutra.
- Drachen, A. Canossa and G.N. Yannakakis, (2009). Player Modeling Using Self-Organization in Tomb Raider: Underworld. Proc. IEEE Symp. Computational Intelligence and Games, pp. 1-8.
- Etheredge M., Lopes R. and Bidarra R. (2013). A Generic Method for Classification of Player Behavior. AAIDE - Artificial Intelligence in the Game Design Process.
- K.S.Y. Chiu, and K.C.C. Chan, (2008). Game engine design using data mining. Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications, pp. 352-357.
- L. R. Rabiner, (1989). A Tutorial on Hidden Markov Models and SelectedApplications in Speech Recognition. Proc. IEEE, Vol.77 (2), pp. 257-285.
- Luis Javier Rodriguez, Ines Torres (2003). Comparative Study of the Baum-Welch and Viterbi Training Algorithms Applied to Read and Spontaneous Speech Recognition.
- S. C. Bakkes, P. H. Spronck, and G. van Lankveld, (2012). Player Behavioural Modelling for Video Games. Entertainment Computing, in press:1--9.
- Shin Jin Kang & Soo Kyun Kim, (2014). Automated spatio-temporal analysis techniques for game environment. Springer Science+Business Media New York.
- Thawonmas, R., Ho, J.Y., and Matsumoto, Y. (2003). Identification of Player Types in Massively Multiplayer Online Games. Proc. the 34th Annual conference of International Simulation and Gaming Association (ISAGA2003), Chiba, Japan, pp. 893-900.
- Yannakakis, G. N.; Spronck, P.; Loiacono, D.; and Andre, E. (2013). Player Modeling. Dagstuhl Seminar on Game Artificial and Computational Intelligence.
- Yoshitaka Matsumoto, Ruck Thawonmas, (2004). MMOG Player Classification Using Hidden Markov Models. ICEC-Entertainment Computing.
Paper Citation
in Harvard Style
Kilic A., Gunes M. and Sariel S. (2016). Player Profiling using Hidden Markov Models Supported with the Sliding Window Method . In Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-172-4, pages 362-369. DOI: 10.5220/0005711403620369
in Bibtex Style
@conference{icaart16,
author={Alper Kilic and Mehmet Akif Gunes and Sanem Sariel},
title={Player Profiling using Hidden Markov Models Supported with the Sliding Window Method},
booktitle={Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2016},
pages={362-369},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005711403620369},
isbn={978-989-758-172-4},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Player Profiling using Hidden Markov Models Supported with the Sliding Window Method
SN - 978-989-758-172-4
AU - Kilic A.
AU - Gunes M.
AU - Sariel S.
PY - 2016
SP - 362
EP - 369
DO - 10.5220/0005711403620369