Our anticipation approach in the robotic soccer domain, as presented below, could be easily generalized.
Consider that for each agent, for each state, and at each time, there is a computable value for the probability that an active agent could successfully collaborate with a passive agent. As the world is constantly changing, the values for the probability of collaboration are computed as a function of the dynamic world.
Assuming that the transitions between states for each agent take time (or other type of cost), then anticipation consists of the selection of a new state that maximizes the probability of future collaboration.
Anticipation therefore allows for a flexible adjustment of a team agent towards the increase of the probability of being useful for the team. We now formally present our anticipation algorithm within the robotic soccer domain.