There has been so many research activities about robot soccer system in the many research fields, for example, intelligent control, communication, computer technology, sensor technology, image processing, mechatronics, artificial life, etc.. Especially strategy for attacking is almost researched in the field of strategy, and developed intelligent strategy for attacking, namely strategy like that of Human soccer. Then, soccer robots cannot defense completely and efficiently by using simple defense strategy. Therefore, intention extraction of attackers is needed for efficient defense.
In this thesis, intention extractor of soccer robots is designed and developed based on FMMNN(Fuzzy Min-Max Neural networks). First, intention for soccer robot system is defined, and intention extraction for soccer robot system is explained. Next, FMMNN is introduced. FMMNN is one of the pattern classification method and have several advantages: on-line adaptation, short training time, soft decision. Therefore, FMMNN is suitable for soccer robot system having dynamic environment. Observer extracts attack intention of opponents by using this intention extractor, and this intention extractor is also used for analyzing strategy of Opponent team. The capability of developed intention extractor is verified by simulation of 3 vs. 3 robot soccer simulator.