Abstract
We propose the use of a Fuzzy Naive Bayes classifier with a MAP rule as a decision making module for the RoboCup Soccer Simulation 3D domain. The Naive Bayes classifier has proven to be effective in a wide range of applications, in spite of the fact that the conditional independence assumption is not met in most cases. In the Naive Bayes classifier, each variable has a finite number of values, but in the RoboCup domain, we must deal with continuous variables. To overcome this issue, we use a fuzzy extension known as the Fuzzy Naive Bayes classifier that generalizes the meaning of an attribute so it does not have exactly one value, but a set of values to a certain degree of truth. We implemented this classifier in a 3D team so an agent could obtain the probabilities of success of the possible action courses given a situation in the field and decide the best action to execute. Specifically, we use the pass evaluation skill as a test bed. The classifier is trained in a scenario where there is one passer, one teammate and one opponent that tries to intercept the ball. We show the performance of the classifier in a test scenario with four opponents and three teammates. After a brief introduction, we present the specific characteristics of our training and test scenarios. Finally, results of our experiments are shown.
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References
Langley, P., Iba, W., Thompson, K.: An Analysis of Bayesian Classifiers. In: Proc. 10th Nat. Conf. on Artificial Intelligence, pp. 223–228. AAAI Press and MIT Press, Cambridge, USA (1992)
Heckerman, D.: A tutorial on learning with bayesian networks. Technical Report MSR-TR-95-06, Microsoft Research, Redmond, Washington (1995)
Androutsopoulos, I., Koutsias, J., Chandrinos, K.V., Paliouras, G., Spyropoulos, C.D.: An Evaluation of Naive Bayesian Anti-Spam Filtering. In: Proceedings of the workshop on Machine Learning in the New Information Age (2000)
Lewis, D.: Naive Bayes at forty: The independence assumption in information retrieval. In: Proceedings of European Conference on Machine Learning, pp. 4–15 (1998)
Tóth, L., Kocsor, A., Csirik, J.: On Naive Bayes in Speech Recognition. Int. J. Appl. Math. Comput. Sci. 15(2), 287–294 (2005)
Sebe, N., Cohen, I., Garg, A., Lew, M.S., Huang, T.S.: Emotion recognition using a Cauchy naive Bayes classifier. In: Proceedings of 16th International Conference on Pattern Recognition, pp. 17–20 (2002)
Demsar, J., Zupan, B., Kattan, M.W., Beck, J.R., Bratko, I.: Naive Bayesian- based nomogram for prediction of prostate cancer recurrence. Medical Informatics Europe 1999, Studies in health technology and informatics 68, 436–441 (1999)
Rish, I.: An empirical study of the naive bayes classifier. In: Proceedings of IJCAI-01 workshop on Empirical Methods in AI, pp. 41–46 (2001)
Friedman, N., Goldszmidt, M.: Discretization of continuous attributes while learning Bayesian networks. In: Saitta, L. (ed.) Proceedings of 13-th International Conference on Machine Learning, pp. 157–165 (1996)
Störr, H.-P.: A compact fuzzy extension of the Naive Bayesian classification algorithm. In: Proceedings InTech/VJFuzzy, pp. 172–177 (2002)
Tang, Y., Pan, W., Li, H., Xu, Y.: Fuzzy Naive Bayes classifier based on Fuzzy Clustering. In: IEEE International Conference on Systems, Man and Cybernetics (2002)
Kitano, H., Asada, M., Kuniyoshi, Y., Noda, I., Osawa, E.: RoboCup: The robot world cup initiative. In: Proceedings of the First International Conference on Autonomous Agents, pp. 340–347 (1997)
Riley, P., Riley, G.: SPADES: - A Distributed Agent Simulation Environment with Software-in-the-Loop Execution. In: Winter Simulation Conference Proceedings, pp. 817–825 (2003)
Buck, S., Riedmiller, M.: Learning Situation Dependent Success Rates Of Action In A RoboCup Scenario. In: Pacific Rim International Conference on Artificial Intelligence, p. 809 (2000)
Mostafa, M.G.-H., Perkins, T.C., Farag, A.A.: A Two-Step Fuzzy-Bayesian Classification for High Dimensional Data. In: ICPR 2000. 15th International Conference on Pattern Recognition, vol. 3, pp. 3421–3424 (2000)
Stone, P.: Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer. MIT Press, Cambridge (2000)
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Bustamante, C., Garrido, L., Soto, R. (2007). Fuzzy Naive Bayesian Classification in RoboSoccer 3D: A Hybrid Approach to Decision Making. In: Lakemeyer, G., Sklar, E., Sorrenti, D.G., Takahashi, T. (eds) RoboCup 2006: Robot Soccer World Cup X. RoboCup 2006. Lecture Notes in Computer Science(), vol 4434. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74024-7_52
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DOI: https://doi.org/10.1007/978-3-540-74024-7_52
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