Anticipatory Counterplanning
This addresses the challenge of proactive counterplanning in AI for competitive scenarios, but it appears incremental as it builds on existing goal inference and planning methods.
The paper tackles the problem of preventing opponents from achieving their goals in competitive environments when the opponent's goal is unknown, and it shows that their Anticipatory Counterplanning algorithm outperforms reactive methods by increasing the chances of stopping opponents.
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.