  This paper reports on the application of sequence analysis algorithms for
agents in robotic soccer and a suitable representation is proposed to achieve
this mapping. The objective of this research is to generate novel better
in-game strategies with the aim of faster adaptation to the changing
environment. A homogeneous non-communicating multi-agent architecture using the
representation is presented. To achieve real-time learning during a game, a
bucket brigade algorithm is used to reinforce Cellular Automata Based
Classifier. A technique for selecting strategies based on sequence analysis is
adopted.
