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Incremental Knowledge Acquisition Using Generalised RDR for Soccer Simulation

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Knowledge Management and Acquisition for Smart Systems and Services (PKAW 2010)

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Abstract

This paper describes a system that allows soccer coaches to specify the behaviour of agents for the Robocup 2D soccer simulation domain [1]. The work we present is based on Generalised Ripple Down Rules [7,2]and allows the coach to interact directly with the system to incrementally model behaviours along with intermediate features during the knowledge acquisition process. The system was evaluated over a period of 6 months to measure the level of performance of the multi-agent teams created with the system and to gather feedback about the usability of the system. During this period the system was successfully used by four soccer coaches with differing levels of soccer and computer expertise. All coaches were able to use the system to develop teams that could play at a world class level against the finalists from the Robocup 2007 2D simulation tournament. The approach we present is general enough to be applied to any complex planning problem, with the requirement that a rich feature language is developed to support the specific domain.

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References

  1. Noda, I., Matsubara, H., Hiraki, K., Frank, I.: Soccer server: A tool for research on mulitagent systems. Applied Artificial Intelligence 12, 233–250 (1998)

    Article  Google Scholar 

  2. Compton, P., Cao, T., Kerr, J.: Generalising Incremental Knowledge Acquisition. In: Proceedings of the Pacific Knowledge Acquisition Workshop (2004)

    Google Scholar 

  3. Stone, P.: Layered Learning in Multi-agent Systems. PhD Thesis, Carnegie Mellon University (1998)

    Google Scholar 

  4. de Boer, R., Kok, J.: The Incremental Development of a Synthetic Mutli-Agent System: The UvA Trilean 2001 Robotic Soccer Simulation Team. Master’s thesis, University of Amsterdam (2002)

    Google Scholar 

  5. Finlayson, A., Compton, P.: Incremental Knowledge Acquisition using RDR for Soccer Simulation. In: Proceedings of the Pacific Knowledge Acquisition Workshop (2004)

    Google Scholar 

  6. Preston, P., Edwards, E., Compton, P.: A 2000 Rule Expert System Without Knowledge Engineers. In: Proceedings of the 8th AAAI-Sponsored Banff Knowledge Acquisition for Knowledge-Based Systems Workshop, Banff, Canada, pp. 17.1–17.10 (1994)

    Google Scholar 

  7. Compton, P., Richards, D.: Generalising Ripple-Down Rules. In: Dieng, R., Corby, O. (eds.) EKAW 2000. LNCS (LNAI), vol. 1937, pp. 380–386. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  8. Suryanto, H., Compton, P.: Invented Knowledge Predicates to Reduce Knowledge Acquisition Effort. In: Tecuci, G., Aha, D., Boicu, M., Cox, M., Ferguson, G. (eds.) Proceedings of the IJCAI 2003 Workshop on Mixed-Initiative Intelligent Systems, Eighteenth International Joint Conference on Artificial Intelligence, Austin tate, Acapulco Mexico, August 9-15, pp. 107–114 (2003)

    Google Scholar 

  9. Kang, B., Compton, P., Preston, P.: Multiple Classification Ripple Down Rules: Evaluation and Possibilities. In: Proceedings of the 9th AAAI-Sponsored Banff Knowledge Acquisition for Knowledge-Based Systems Workshop, Banff, Canada, pp. 17.1–17.20. University of Calgary (1995)

    Google Scholar 

  10. Shiraz, G., Sammut, C.: Acquiring Control Knowledge from Examples Using Ripple-down Rules and Machine Learning. In: Gaines, B.R., Musen, M. (eds.) Proceedings of Eleventh Workshop on Knowledge Acquisition, Modelgin and Management (KAW 1998), Banff, Alberta Canada, April 18-23, pp. KAT-5.1–KAT-5.17. University of Calgary, Calgary (1990)

    Google Scholar 

  11. Compton, P., Ramadan, Z., Preston, P., Le-Gia, T., Chellen, V., Mullholland, M.: A trade-off between domain knowledge and problem-solving method power. In: Gaines, B., Musen, M. (eds.) 11th Banff Knowledge Acquisition for Knowledge-Based Systems Workshop, SHARE, Banff, vol. 17, pp. 1–19. SRDG Publications/University of Calgary (1999)

    Google Scholar 

  12. Richards, D., Compton, P.: Revisiting Sisyphus I - an Incremental Approach to Resource Allocation Using Ripple-Down Rules. In: Gaines, B., Kremer, R., Musen, M. (eds.) 12th Banff Knowledge Acquisition for Knowledge-Based Systems Workshop, Banff, pp. 7-7.1–7.20. SRDG Publications/University of Calgary (1999)

    Google Scholar 

  13. Beydoun, G., Hoffman, A.: Incremental Acquisition of Search Knowledge. International Journal of Human Computer Studies 52(3), 493–530 (2000)

    Article  Google Scholar 

  14. Compton, P., Horn, R., Quinlan, R., Lazarus, L.: Maintaining an expert system in J. R. Quinlan. In: Applications of Expert Systems, pp. 366–385. Addison Wesley, London (1989)

    Google Scholar 

  15. Kang, B., Yoshida, K., Motoda, H., Compton, P.: A help desk system with Intelligent Interface. Applied Artificial Intelligence 11(7-8), 611–631 (1997)

    Article  Google Scholar 

  16. Obst, O., Bodecker, J.: Flexible Coordination of Multiagent Team Behaviour Using HTN Planning. In: Bredenfeld, A., Jacoff, A., Noda, I., Takahashi, Y. (eds.) RoboCup 2005. LNCS (LNAI), vol. 4020, pp. 521–528. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  17. CMU RoboCup Simulator Team Homepage: Carnegie Mellon University, Pittsburgh, PA (2003), http://www-2.cs.cmu.edu/~pstone/RoboCup/CMUnited-sim.html

  18. The Official UvA Trilearn Website: University of Amsterdam (2001), http://www.science.uva.nl/~jellekok/robocup

  19. Brainstormers base source code, http://www.ni.uos.de

  20. Helios base source code, http://sourceforge.jp/projects/rctools

  21. Riedmiller, M., Merke, A.: Using Machine Learning Techniques in Complex Multi-Agent Domains. In: Stamatescu, I., Menzel, W., Richter, M., Ratsch, U. (eds.) Perspectives on Adaptivity and Learning. LNCS. Springer, Heidelberg (2002)

    Google Scholar 

  22. Kok, J., Spaan, M., Vlassis, N.: Multi-robot decision making using coordination graphs. In: de Almeida, A.T., Nunes, U. (eds.) Proceedings of the 11th International Conference on Advanced Robotics, ICAR 2003, Coimbra, Portugal, June 30-July 3, pp. 1124–1129 (2003)

    Google Scholar 

  23. Yunpeng, C., Jiang, C., Jinyi, Y., Li, S.: Global Planning from Local Eyeshot: An Implementation of Observation -based Plan Coordination in RoboCup Simulation Games. In: Birk, A., Coradeschi, S., Tadokoro, S. (eds.) RoboCup 2001. LNCS (LNAI), vol. 2377, p. 12. Springer, Heidelberg (2002)

    Google Scholar 

  24. Buttinger, S., Diedrich, M., Hennig, L., Honemann, A., Hugelmeyer, P., Nie, A., Pegam, A., Rogowski, C., Rollinger, C., Steffens, T., Teiken, W.: The ORCA Project Report, http://www.cl-ki.uni-osnabrueck.de/~tsteffen/orcapub.html

  25. Scerri, P., Coradeschi, S., Torne, A.: A user oriented system for developing behaviour based agents. In: Asada, M., Kitano, H. (eds.) RoboCup 1998. LNCS (LNAI), vol. 1604, pp. 173–186. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  26. Compton, P., Preston, P., Kang, B., Yip, T.: Local patching produces compact knowledge bases. In: Steels, L., Van de Velde, W., Schreiber, G. (eds.) EKAW 1994. LNCS, vol. 867, pp. 103–117. Springer, Heidelberg (1994)

    Google Scholar 

  27. Suryanto, H., Richards, D., Compton, P.: The Automatic Compression of Multiple Classification Ripple Down Rule Knowledge Base Systems: Preliminary Experiments. In: Proceedings of the Third International Conference on Knowledge-Based Intelligent Information Engineering Systems, L. Jain Adelaide, pp. 203–206 (1999)

    Google Scholar 

  28. Edwards, G., Compton, P., Malor, R., Srinivasan, A., Lazarus, L.: PEIRS: a pathologist maintained expert system for the interpretation of chemical pathology reports. Pathology 25, 27–34 (1993)

    Article  Google Scholar 

  29. Asakawa, H., Ueda, M., Yamazaki, Y., Takeuchi, I.: YowAI 2007 Team Description. In: RoboCup 2007. LNCS (LNAI), vol. 2377. Springer, Heidelberg (2007)

    Google Scholar 

  30. Berger, R., Burkhard, H.: AT Humboldt Team Description 2007. In: Robocup 2007. LNCS (LNAI), vol. 2377. Springer, Heidelberg (2007)

    Google Scholar 

  31. Norouzitallab, M.: Nemesis 2D - Team Description 2007. In: Robocup 2007. LNCS (LNAI), vol. 2377. Springer, Heidelberg (2007)

    Google Scholar 

  32. Akiyama, H.: HELIOS 2007 Team Description. In: RoboCup 2007. LNCS (LNAI), vol. 2377. Springer, Heidelberg (2007)

    Google Scholar 

  33. Riedmiller, M., Gabel, T.: Brainstormers 2D Team Description 2007. In: RoboCup 2007. LNCS (LNAI), vol. 2377. Springer, Heidelberg (2007)

    Google Scholar 

  34. Kitano, H., Kuniyoshi, M., Noda, Y., Osawa, E.: RoboCup: The Robot World Cup Initiative. In: The First International Conference on Autonomous Agents (1997)

    Google Scholar 

  35. Gspandl, S., Monichi, D., Reip, M., Steinbauer, G., Wolfram, M., Zehentnerm, C.: KickOffTug - Team Description Paper 2007. In: Robocup 2007. LNCS (LNAI), vol. 2377. Springer, Heidelberg (2007)

    Google Scholar 

  36. Bekmann, J., Hoffman, A.: Improved Knowledge Acquisition for High Performance Heuristic Search. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 41–46 (2005)

    Google Scholar 

  37. Kerr, J., Compton, P.: Toward Generic Model-Based Object Recognition by Knowledge Acquisition and Machine Learning. In: Workshop on Mixed-Initiative Intelligent Systems, Int. Joint Conf. on AI (2003)

    Google Scholar 

  38. Singh, P., Compton, P.: Combining machine learning and heuristic rules using GRDR for detection of honeycombing in HRCT lung images. In: 9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems,

    Google Scholar 

  39. Kwok, R.: Using Ripple Down Rules for Actions and Planning. In: Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence (2002)

    Google Scholar 

  40. Compton, P., Jansen, B.: A philosophical basis for knowledge acquisition. Knowledge Acquisition 2, 241–257 (1990)

    Article  Google Scholar 

  41. Robocup 2007 Soccer Simulation Competition (2007), http://wiki.cc.gatech.edu/robocup/index.php/Soccer_Simulation

  42. Yoshida, T., Wada, T., Motoda, H., Washio, T.: Adaptive Ripple Down Rules method based on minimum description length principle. Intelligent Data Analysis 8(3) (2004)

    Google Scholar 

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Finlayson, A., Compton, P. (2010). Incremental Knowledge Acquisition Using Generalised RDR for Soccer Simulation. In: Kang, BH., Richards, D. (eds) Knowledge Management and Acquisition for Smart Systems and Services. PKAW 2010. Lecture Notes in Computer Science(), vol 6232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15037-1_13

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  • DOI: https://doi.org/10.1007/978-3-642-15037-1_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15036-4

  • Online ISBN: 978-3-642-15037-1

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