Survey on Online Adversarial Planning for Real-time Strategy Game

Abstract

Real-time strategy game online adversarial planning is a challenging problem in the field of multi-agent learning.In the process of game confrontation,in the face of an uncertain threat environment and non-stationary opponents,the agent needs to reason about the opponent’s actions within a limited time according to the game situation,make your own action plan quickly and perform adversarial planning in the huge state space and action space.The real-time strategy game platform is an ideal testbed for studying online adversarial planning problems.This paper firstly uses a typical real-time strategy game model to elicit the real-time strategy game confrontation problems,and classifies them into three levels and two operation control methods,and sorts out the five challenges faced from five sub-directions.Secondly,the current online adversarial planning methods are comprehensively reviewed and analyzed from three perspectives of tactical adversarial planning,strategic adversarial planning and mixed adversarial planning.Finally,the key issues that need to be studied in the future are pointed out from three key aspects:opponent and player modeling,human-machine collaborative online ad hoc planning,and learning-based planning.

Keywords

• rts game|online adversarial planning|monte carlo tree search|hierarchical task networks|opponent modeling|human-machine collaboration