Interception strategy in Multi-Agent Systems based on Rough Sets

Multi-agent systems (MAS) have emerged as an active sub field of artificial intelligence. Robotic soccer is a typical multi-agent systems, wherein the challenge is to develop and hone the skills of the agents that take part in the game. This paper proposes an interception strategy based on rough sets with dominance relation, in which the methods of knowledge reduction and rule extraction are investigated to make interception decision. Then an illustrative example from RoboCup simulator game is analyzed to show the validity of the proposed strategy as well as future research directions.