Abstract
In Multi-Agent System, observing other agents and modelling their behaviour represents an essential task: agents must be able to quickly adapt to the environment and infer knowledge from other agents’ deportment. The observed data from this kind of environments are inherently sequential. We present a relational model to characterise adversary teams based on its behaviour using a set of relational sequences in order to classify them. We propose to use a relational learning algorithm to mine meaningful features as frequent patterns among the relational sequences and use these features to construct a feature vector for each sequence and then to compute a similarity value between sequences. The sequence extraction and classification are implemented in the domain of simulated robotic soccer, and experimental results are presented.
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References
Kitano, H., Tambe, M., Stone, P., Veloso, M., Coradeschi, S., Osawa, E., Matsubara, H., Noda, I., Asada, M.: The robocup synthetic agent challenge 97. In: IJCAI 1997: Proceedings of the 15th international joint conference on Artifical intelligence, pp. 24–29. Morgan Kaufmann Publishers Inc., San Francisco (1997)
Riley, P., Veloso, M.: Planning for distributed execution through use of probabilistic opponent models, pp. 72–81 (2002)
Riley, P., Veloso, M.M.: On behavior classification in adversarial environments. In: Parker, L.E., Bekey, G.A., Barhen, J. (eds.) DARS, pp. 371–380. Springer, Heidelberg (2000)
Steffens, T.: Feature-based declarative opponent-modelling. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003. LNCS (LNAI), vol. 3020, pp. 125–136. Springer, Heidelberg (2004)
Steffens, T.: Similarity-based opponent modelling using imperfect domain theories. In: CIG (2005)
Kaminka, G.A., Fidanboylu, M., Chang, A., Veloso, M.M.: Learning the sequential coordinated behavior of teams from observations. In: Kaminka, G.A., Lima, P.U., Rojas, R. (eds.) RoboCup 2002. LNCS (LNAI), vol. 2752, pp. 111–125. Springer, Heidelberg (2003)
Ledezma, A., Aler, R., Sanchis, A., Borrajo, D.: Predicting opponent actions by observation, pp. 286–296 (2005)
Lattner, A.D., Miene, A., Visser, U., Herzog, O.: Sequential pattern mining for situation and behavior prediction in simulated robotic soccer. In: Bredenfeld, A., Jacoff, A., Noda, I., Takahashi, Y. (eds.) RoboCup 2005. LNCS (LNAI), vol. 4020, pp. 118–129. Springer, Heidelberg (2006)
Iglesias, J.A., Ledezma, A., Sanchis, A., Kaminka, G.A.: Classifying efficiently the behavior of a soccer team. In: Burgard, W., et al. (eds.) Intelligent Autonomous Systems, IAS-10, vol. 10, pp. 316–323 (2008)
Ullman, J.: Principles of Database and Knowledge-Base Systems, vol. I. Computer Science Pres (1988)
Muggleton, S., De Raedt, L.: Inductive logic programming: Theory and methods. Journal of Logic Programming 19/20, 629–679 (1994)
Esposito, F., Di Mauro, N., Basile, T.M.A., Ferilli, S.: Multi-dimensional relational sequence mining. Fundam. Inf. 89(1), 23–43 (2009)
Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I.: Fast discovery of association rules, pp. 307–328. American Association for Artificial Intelligence, Menlo Park (1996)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley-Interscience, Hoboken (November 2000)
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Bombini, G., Di Mauro, N., Ferilli, S., Esposito, F. (2010). Classifying Agent Behaviour through Relational Sequential Patterns. In: Jędrzejowicz, P., Nguyen, N.T., Howlet, R.J., Jain, L.C. (eds) Agent and Multi-Agent Systems: Technologies and Applications. KES-AMSTA 2010. Lecture Notes in Computer Science(), vol 6070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13480-7_29
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DOI: https://doi.org/10.1007/978-3-642-13480-7_29
Publisher Name: Springer, Berlin, Heidelberg
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