Robot Soccer - Strategy Description And Game Analysis

The robot soccer game, as a part of standard applications of distributed system control in real time, provides numerous opportunities for the application of AI. Real-time dynamic strategy description and strategy learning possibility based on game observation are important to discover opponent’s strategies, search tactical group movements and synthesize proper counter-strategies. In this paper, the game is separated into physical part and logical part including strategy level and abstract level. Correspondingly, the game strategy description and prediction of ball motion are built up. The way to use this description, such as learning rules and adapting team strategies to every single opponent, is also discussed. Cluster analysis is used to validate the strategy extraction.

[1]  Anita L. Feller.  Understanding Search Engines , 2012 .

[2]  Peter Stone,et al.  Individual and collaborative behaviors in a team of homogeneous robotic soccer agents , 1998, Proceedings International Conference on Multi Agent Systems (Cat. No.98EX160).

[3]  Raquel Ros,et al.  Acquiring a Robust Case Base for the Robot Soccer Domain , 2007, IJCAI.

[4]  Sergey Serebryakov,et al.  Data Mining Techniques for RoboCup Soccer Agents , 2005, AIS-ADM.

[5]  Ivan Bratko,et al.  Multi-agent strategic modeling in a robotic soccer domain , 2006, AAMAS '06.

[6]  Pablo Javier Alsina,et al.  Recognizing behaviors patterns in a micro robot soccer game , 2005, Fifth International Conference on Hybrid Intelligent Systems (HIS'05).

[7]  Jong-Hwan Kim,et al.  Soccer Robotics , 2004, Springer Tracts in Advanced Robotics.

[8]  Gregor Lämmel,et al.  Exploiting Past Experience - Case-Based Decision Support for Soccer Agents , 2007, KI.

[9]  Gourab Sen Gupta,et al.  Strategy for collaboration in robot soccer , 2002, Proceedings First IEEE International Workshop on Electronic Design, Test and Applications '2002.

[10]  Xiao-Jun Zhao,et al.  Research on Strategy of Robot Soccer Game Based on Opponent Information , 2006, 2006 International Conference on Machine Learning and Cybernetics.

[11]  Manuela M. Veloso,et al.  Team Playing Behavior in Robot Soccer: A Case-Based Reasoning Approach , 2007, ICCBR.

[12]  Vilém Srovnal,et al.  Multicriterial Decision-Making in Multiagent Systems , 2006, International Conference on Computational Science.

[13]  Ivan Bratko,et al.  Learning long-term chess strategies from databases , 2006, Machine Learning.

[14]  Václav Snásel,et al.  Strategy Description for Mobile Embedded Control Systems Exploiting the Multi-agent Technology , 2007, International Conference on Computational Science.

[15]  Amin Milani Fard,et al.  Game Theory-based Data Mining Technique for Strategy Making of a Soccer Simulation Coach Agent , 2007, ISTA.

[16]  Andraz Bezek,et al.  Discovering strategic multi-agent behavior in a robotic soccer domain , 2005, AAMAS '05.

[17]  R. Whittington,et al.  Exploring Corporate Strategy: Text and Cases , 1989 .

[18]  Risto Miikkulainen,et al.  Evolving Soccer Keepaway Players Through Task Decomposition , 2005, Machine Learning.