Anticipation as a key for collaboration in a team of agents: a case study in robotic soccer

We investigate teams of compete autonomous agents that can collaborate towards achieving precise objectives in an adversarial dynamic environment. We have pursued these two frameworks emphasizing their different technical challenges. Creating effective members of a team is a challenging research problem. We first address this issue by introducing a team architecture organization which allows for a rich task decomposition between team members. The main contribution of this paper is our introduction of an action- selection algorithm that allows for a teammate to anticipate the needs of other teammates. Anticipation is critical for maximizing the probability of successful collaboration in teams of agents. We show how our contribution applies to the two concrete robotic soccer frameworks and present controlled empirical result run in simulation. Anticipation was successfully used by both our CMUnited-98 simulator and CMUnited-98 small-robot teams in the RoboCup-98 competition. The two teams are RoboCup-98 world champions each in its own league.