Abstract
Case-base reasoning in a real-time context requires the system to output the solution to a given problem in a predictable and usually very fast time frame. As the number of cases that can be processed is limited by the real-time constraint, we explore ways of selecting the most important cases and ways of speeding up case comparisons by optimizing the representation of each case. We focus on spatially-aware systems such as mobile robotic applications and the particular challenges in representing the systems’ spatial environment. We select and combine techniques for feature selection, clustering and prototyping that are applicable in this particular context and report results from a case study with a simulated RoboCup soccer-playing agent. Our results demonstrate that preprocessing such case bases can significantly improve the imitative ability of an agent.
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References
Smyth, B.: Case-base maintenance. In: Proceedings of the Eleventh International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (1998)
RoboCup: Robocup online (2008), http://www.robocup.org
Floyd, M.W., Esfandiari, B., Lam, K.: A case-based approach to imitating robocup players. In: Twenty-First International FLAIRS Conference, pp. 251–256 (2008)
Lam, K., Esfandiari, B., Tudino, D.: A scene-based imitation framework for robocup clients. In: Proceedings of the Workshop MOO at AAMAS 2006 (2006)
Davoust, A., Floyd, M.W., Esfandiari, B.: Use of fuzzy histograms to model the spatial distribution of objects in case-based reasoning. In: Bergler, S. (ed.) Canadian Conference on AI, pp. 72–83. Springer, Heidelberg (2008)
Karol, A., Nebel, B., Stanton, C., Williams, M.A.: Case based game play in the robocup four-legged league part i the theoretical model. In: RoboCup (2003)
Moravec, H., Elfes, A.E.: High resolution maps from wide angle sonar. In: Proceedings of the 1985 IEEE International Conference on Robotics and Automation, pp. 116–121 (1985)
Langner, K.: The Krislet Java Client (1999), http://www.ida.liu.se/frehe/RoboCup/Libs
Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence 97(1-2), 273–324 (1997)
John, G.H., Kohavi, R., Pfleger, K.: Irrelevant features and the subset selection problem. In: Proceedings of the Eleventh ICML, pp. 121–129 (1994)
Aha, D.W., Bankert, R.L.: A comparative evaluation of sequential feature selection algorithms. Learning from Data: AI and Statistics V, 199–206 (1996)
Xu, R., Wunsch, D.I.I.: Survey of clustering algorithms. IEEE Transactions on Neural Networks 16(3), 645–678 (2005)
Bicego, M., Murino, V., Figueiredo, M.A.T.: Similarity-based clustering of sequences using hidden markov models. In: Perner, P., Rosenfeld, A. (eds.) MLDM 2003. LNCS, vol. 2734, pp. 86–95. Springer, Heidelberg (2003)
Dubnov, S., El-Yaniv, R., Gdalyahu, Y., Schneidman, E., Tishby, N., Yona, G.: A new nonparametric pairwise clustering algorithm based on iterative estimation of distance profiles. Mach. Learn. 47(1), 35–61 (2002)
Hartigan, J.A.: Clustering Algorithms. John Wiley & Sons, Inc., New York (1975)
Berger, R., Lämmel, G.: Exploiting past experience – case-based decision support for soccer agents. In: Hertzberg, J., Beetz, M., Englert, R. (eds.) KI 2007. LNCS (LNAI), vol. 4667. Springer, Heidelberg (2007)
Steffens, T.: Adapting similarity measures to agent types in opponent modeling. In: Proceedings of the Workshop MOO at AAMAS 2004, pp. 125–128 (2004)
Ahmadi, M., Lamjiri, A.K., Nevisi, M.M., Habibi, J., Badie, K.: Using a two-layered case-based reasoning for prediction in soccer coach. In: Proceedings of the MLMTA 2003, Las Vegas, Nevada, pp. 181–185 (2003)
Marling, C., Tomko, M., Gillen, M., Alexander, D., Chelberg, D.: Case-based reasoning for planning and world modeling in the robocup small sized league. In: IJCAI Workshop on Issues in Designing Physical Agents for Dynamic Real-Time Environments (2003)
Ros, R., de Mántaras, R.L., Arcos, J.L., Veloso, M.: Team playing behavior in robot soccer: A case-based approach. In: Weber, R.O., Richter, M.M. (eds.) ICCBR 2007. LNCS (LNAI), vol. 4626, pp. 46–60. Springer, Heidelberg (2007)
Wettschereck, D., Aha, D.W.: Weighting features. In: First International CBR Research and Development Conference, pp. 347–358. Springer, Berlin (1995)
Jarmulak, J., Craw, S., Crowe, R.: Genetic algorithms to optimise CBR retrieval. In: 5th European Workshop on Advances in CBR, pp. 136–147 (2000)
Maximini, K., Maximini, R., Bergmann, R.: An investigation of generalized cases. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS, vol. 2689, pp. 261–275. Springer, Heidelberg (2003)
Lenz, M., Burkhard, H.D.: Case retrieval nets: Basic ideas and extensions. Kunstliche Intelligenz, 227–239 (1996)
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Floyd, M.W., Davoust, A., Esfandiari, B. (2008). Considerations for Real-Time Spatially-Aware Case-Based Reasoning: A Case Study in Robotic Soccer Imitation. In: Althoff, KD., Bergmann, R., Minor, M., Hanft, A. (eds) Advances in Case-Based Reasoning. ECCBR 2008. Lecture Notes in Computer Science(), vol 5239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85502-6_13
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DOI: https://doi.org/10.1007/978-3-540-85502-6_13
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