Heuristic Search Exploiting Non-additive and Unit Properties for RTS-game Unit Micromanagement

This paper presents an approach that integrates fuzzy integral and fast heuristic search for improving the quality of unit micromanagement in the popular RTS game StarCraft. Unit micromanagement, i.e., detailed control of units in combat, is one of the most challenging problems posed by RTS games and is often tackled with search algorithms such as Minimax or Alpha-Beta. Due to vast state and action spaces, the game tree is often very large, and search algorithms must rely on evaluation methods from a certain limited depth rather than exploring deeper into the tree. We therefore attempt to apply fuzzy integral and aim for an evaluation method with high accuracy in the search. To achieve this aim, we propose a new function that allows fuzzy integral to cope with not only non-additive properties but also unit properties in RTS games. Experimental results are reported at the end of this paper, showing that our approach outperforms an existing approach in terns of win rates in this domain.

[1]  Michael Buro,et al.  Adversarial Planning Through Strategy Simulation , 2007, 2007 IEEE Symposium on Computational Intelligence and Games.

[2]  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).

[3]  Michael Buro,et al.  Predicting Army Combat Outcomes in StarCraft , 2013, AIIDE.

[4]  M. K. Luhandjula Fuzzy optimization: Milestones and perspectives , 2015, Fuzzy Sets Syst..

[5]  G. Klir,et al.  Fuzzy Measure Theory , 1993 .

[6]  Weldon A. Lodwick,et al.  Fuzzy Optimization - Recent Advances and Applications , 2010, Studies in Fuzziness and Soft Computing.

[7]  Ashwin Ram,et al.  Transfer Learning in Real-Time Strategy Games Using Hybrid CBR/RL , 2007, IJCAI.

[8]  Yingjie Li Integrating genetic algorithm and fuzzy integral for evaluating game units combination in RTS game , 2012 .

[9]  Michael Buro,et al.  Incorporating Search Algorithms into RTS Game Agents , 2012 .

[10]  Jonathan Schaeffer,et al.  Monte Carlo Planning in RTS Games , 2005, CIG.

[11]  Santiago Ontañón,et al.  The Combinatorial Multi-Armed Bandit Problem and Its Application to Real-Time Strategy Games , 2013, AIIDE.

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

[13]  Tung Nguyen,et al.  Potential flow for unit positioning during combat in StarCraft , 2013, 2013 IEEE 2nd Global Conference on Consumer Electronics (GCCE).

[14]  Simon C. K. Shiu,et al.  Apply different fuzzy integrals in unit selection problem of real time strategy game , 2011, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011).

[15]  Zhe Wang,et al.  Monte-Carlo planning for unit control in StarCraft , 2012, The 1st IEEE Global Conference on Consumer Electronics 2012.

[16]  Michael Buro,et al.  Portfolio greedy search and simulation for large-scale combat in starcraft , 2013, 2013 IEEE Conference on Computational Inteligence in Games (CIG).

[17]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[18]  Gabriel Synnaeve,et al.  A Bayesian model for opening prediction in RTS games with application to StarCraft , 2011, 2011 IEEE Conference on Computational Intelligence and Games (CIG'11).

[19]  Arnav Jhala,et al.  Building Human-Level AI for Real-Time Strategy Games , 2011, AAAI Fall Symposium: Advances in Cognitive Systems.