Replay-based strategy prediction and build order adaptation for StarCraft AI bots

Ho Chul Cho, Kyung Joong Kim, Sung-Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Citations (Scopus)

Abstract

StarCraft is a real-time strategy (RTS) game and the choice of strategy has big impact on the final results of the game. For human players, the most important thing in the game is to select the strategy in the early stage of the game. Also, it is important to recognize the opponent's strategy as quickly as possible. Because of the 'fog-of-war' in the game, the player should send a scouting unit to opponent's hidden territory and the player predicts the types of strategy from the partially observed information. Usually, expert players are familiar with the relationships between two build orders and they can change the current build order if his choice is not strong to the opponent's strategy. However, players in AI competitions show quite different behaviors compared to the human leagues. For example, they usually have a pre-selected build order and rarely change their order during the game. In fact, the computer players have little interest in recognizing opponent's strategy and scouting units are used in a limited manner. The reason is that the implementation of scouting behavior and the change of build order from the scouting vision is not a trivial problem. In this paper, we propose to use replays to predict the strategy of players and make decision on the change of build orders. Experimental results on the public replay files show that the proposed method predicts opponent's strategy accurately and increases the chance of winning in the game.

Original languageEnglish
Title of host publication2013 IEEE Conference on Computational Intelligence in Games, CIG 2013
DOIs
Publication statusPublished - 2013 Dec 1
Event2013 IEEE Conference on Computational Intelligence in Games, CIG 2013 - Niagara Falls, ON, Canada
Duration: 2013 Aug 112013 Aug 13

Publication series

NameIEEE Conference on Computatonal Intelligence and Games, CIG
ISSN (Print)2325-4270
ISSN (Electronic)2325-4289

Other

Other2013 IEEE Conference on Computational Intelligence in Games, CIG 2013
CountryCanada
CityNiagara Falls, ON
Period13/8/1113/8/13

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All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Software

Cite this

Cho, H. C., Kim, K. J., & Cho, S-B. (2013). Replay-based strategy prediction and build order adaptation for StarCraft AI bots. In 2013 IEEE Conference on Computational Intelligence in Games, CIG 2013 [6633666] (IEEE Conference on Computatonal Intelligence and Games, CIG). https://doi.org/10.1109/CIG.2013.6633666
Cho, Ho Chul ; Kim, Kyung Joong ; Cho, Sung-Bae. / Replay-based strategy prediction and build order adaptation for StarCraft AI bots. 2013 IEEE Conference on Computational Intelligence in Games, CIG 2013. 2013. (IEEE Conference on Computatonal Intelligence and Games, CIG).
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Cho, HC, Kim, KJ & Cho, S-B 2013, Replay-based strategy prediction and build order adaptation for StarCraft AI bots. in 2013 IEEE Conference on Computational Intelligence in Games, CIG 2013., 6633666, IEEE Conference on Computatonal Intelligence and Games, CIG, 2013 IEEE Conference on Computational Intelligence in Games, CIG 2013, Niagara Falls, ON, Canada, 13/8/11. https://doi.org/10.1109/CIG.2013.6633666

Replay-based strategy prediction and build order adaptation for StarCraft AI bots. / Cho, Ho Chul; Kim, Kyung Joong; Cho, Sung-Bae.

2013 IEEE Conference on Computational Intelligence in Games, CIG 2013. 2013. 6633666 (IEEE Conference on Computatonal Intelligence and Games, CIG).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Cho HC, Kim KJ, Cho S-B. Replay-based strategy prediction and build order adaptation for StarCraft AI bots. In 2013 IEEE Conference on Computational Intelligence in Games, CIG 2013. 2013. 6633666. (IEEE Conference on Computatonal Intelligence and Games, CIG). https://doi.org/10.1109/CIG.2013.6633666