Abstract
In this paper, we describe our work on classification of players in Massively Multiplayer Online Games using Hidden Markov Models based on player action sequences. In our previous work, we have discussed a classification approach using a variant of Memory Based Reasoning based on player action frequencies. That approach, however, does not exploit time structures hidden in action sequences of the players. The experimental results given in this paper show that Hidden Markov Models have higher recognition performance than our previous approach, especially for classification of players of different types but having similar action frequencies.
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Matsumoto, Y., Thawonmas, R. (2004). MMOG Player Classification Using Hidden Markov Models. In: Rauterberg, M. (eds) Entertainment Computing – ICEC 2004. ICEC 2004. Lecture Notes in Computer Science, vol 3166. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28643-1_55
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DOI: https://doi.org/10.1007/978-3-540-28643-1_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22947-6
Online ISBN: 978-3-540-28643-1
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