Anticipatory Counterplanning

TL;DR

This paper introduces Anticipatory Counterplanning, a domain-independent algorithm that proactively infers opponents' goals and computes counter strategies, outperforming reactive methods in preventing goal achievement in competitive scenarios.

Contribution

The paper presents a novel, domain-independent algorithm that combines goal inference with planning centroids for proactive counterstrategies in unknown-goal environments.

Findings

Outperforms reactive counterplanning in experiments

Increases success rate in preventing opponents' goals

Effective in domain-independent scenarios

Abstract

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.