Cellular Automata based Feedback Mechanism in Strengthening biological Sequence Analysis Approach to Robotic Soccer
This is an incremental improvement for robotic soccer agents, focusing on strategy generation and adaptation.
The paper tackled the problem of generating better in-game strategies for robotic soccer agents by applying sequence analysis algorithms and a cellular automata-based classifier with a bucket brigade algorithm for real-time learning, resulting in faster adaptation to changing environments.
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.