  In competitive environments, commonly agents try to prevent opponents from
achieving their goals. Most previous preventing approaches assume the
opponent's goal is known a priori. Others only start executing actions once the
opponent's goal has been inferred. In this work we introduce a novel
domain-independent algorithm called Anticipatory Counterplanning. It combines
inference of opponent's goals with computation of planning centroids to yield
proactive counter strategies in problems where the opponent's goal is unknown.
Experimental results show how this novel technique outperforms reactive
counterplanning, increasing the chances of stopping the opponent from achieving
its goals.
