Potential flows for controlling scout units in StarCraft

Real-Time Strategy (RTS) games typically take place in a war-like setting and are accompanied with complicated game play. They are not only difficult for human players to master, but also provide a challenging platform for AI research. In a typical RTS game, such as StarCraft, WarCraft, or Age of Empires, knowing what the opponent is doing is a great advantage and sometimes an important key to win the game. For that, good scouting is required. As subsequent work for improving the scouting agent in our StarCraft AI bot-IceBot-the winner of the mixed division in Student StarCraft AI Tournament 2012, this paper proposes a method that applies potential flows to controlling scout units in StarCraft. The proposed method outperforms an existing scouting method as well as a modified version of this existing method and is comparable to scouting by human players.

[1]  D.M. Bevly,et al.  Harmonic potential field path planning for high speed vehicles , 2008, 2008 American Control Conference.

[2]  Ruck Thawonmas,et al.  USING MONTE-CARLO PLANNING FOR MICRO-MANAGEMENT IN STARCRAFT , 2012 .

[3]  Gabriel Synnaeve,et al.  A Bayesian model for RTS units control applied to StarCraft , 2011, 2011 IEEE Conference on Computational Intelligence and Games (CIG'11).

[4]  Arnav Jhala,et al.  A Particle Model for State Estimation in Real-Time Strategy Games , 2011, AIIDE.

[5]  S. Kawamura,et al.  New navigation function utilizing hydrodynamic potential for mobile robot , 1990, Proceedings of the IEEE International Workshop on Intelligent Motion Control.

[6]  Michael Buro,et al.  Fast Heuristic Search for RTS Game Combat Scenarios , 2012, AIIDE.

[7]  Ian D. Watson,et al.  Applying reinforcement learning to small scale combat in the real-time strategy game StarCraft:Broodwar , 2012, 2012 IEEE Conference on Computational Intelligence and Games (CIG).

[8]  Kyung-Joong Kim,et al.  Prediction of early stage opponents strategy for StarCraft AI using scouting and machine learning , 2012, WASA '12.

[9]  Oussama Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Autonomous Robot Vehicles.

[10]  Johan Hagelbäck,et al.  Potential-field based navigation in StarCraft , 2012, 2012 IEEE Conference on Computational Intelligence and Games (CIG).

[11]  Richard M. Murray,et al.  Vehicle motion planning using stream functions , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).