StarCraft Strategy Classification of a Large Human versus Human Game Replay Dataset

• Autorzy: Krištofík Štefan , Malík Peter , Kasáš Matúš

Identyfikatory

DOI

10.15439/2021F48

• Konferencja: Federated Conference on Computer Science and Information Systems (16 ; 02-05.09.2021 ; online)
• Języki publikacji: EN

Abstrakty

EN

Real-time strategy games are popular in AI research and education. Among them, Starcraft: Brood War (SCBW) is particularly well known. Recently, the largest known SCBW game replay dataset STARDATA was published. We classify player strategies used in the dataset for all 3 playable races and all 6 match-ups. We focus on early to mid-game strategies in matches less than 15 minutes long. By mapping the classified strategies to replay files, we label the files of the dataset and make the labeled dataset available.

Słowa kluczowe

EN

player strategy; match; StarCraft; real-time strategy game

PL

strategia gracza; rozgrywka; StarCraft; gra strategiczna; RTS

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2021
• Tom: Vol. 25
• Strony: 137--140
• Opis fizyczny: Bibliogr. 9 poz., rys., tab., wykr.

Twórcy

autor

Krištofík Štefan

• Institute of Informatics, Slovak Academy of Sciences Dúbravská cesta 9, 845 07 Bratislava, Slovakia

autor

Malík Peter

• Institute of Informatics, Slovak Academy of Sciences Dúbravská cesta 9, 845 07 Bratislava, Slovakia

autor

Kasáš Matúš

• Tatramed Software Líščie údolie 9, 841 01 Bratislava, Slovakia

Bibliografia

• 1. Z. Lin, J. Gehring, V. Khalidov and G. Synnaeve, “STARDATA: A StarCraft AI Research Dataset,” 13th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2017, pp. 50–56, https://arxiv.org/abs/1708.02139.
• 2. S. Ontañon, G. Synnaeve, A. Uriarte, F. Richoux, D. Churchill and M. Preuss, “A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft,” IEEE Transactions on Computational Intelligence and AI in games, IEEE Computational Intelligence Society, 2013, 5(4), pp. 1–19, http://dx.doi.org/10.1109/TCIAIG.2013.2286295.
• 3. Mi. Čertický, D. Churchill, K.-J. Kim, Ma. Čertický and R. Kelly, “StarCraft AI Competitions, Bots and Tournament Manager Software,” IEEE Transaction on Games, 2018, 11(3), pp. 227–237, http://dx.doi.org/10.1109/TG.2018.2883499.
• 4. O. Vinyals, I. Babuschkin et al., “Grandmaster level in StarCraft II using multi-agent reinforcement learning,” Nature, 2019, 575, pp. 350–354, http://dx.doi.org/10.1038/s41586-019-1724-z.
• 5. B. G. Weber and M. Mateas, “A data mining approach to strategy prediction,” IEEE Symposium on Computational Intelligence and Games, 2009, pp. 140-147, http://dx.doi.org/10.1109/CIG.2009.5286483.
• 6. H. C. Cho, K. J. Kim and S. B. Cho, “Replay-based strategy prediction and build order adaptation for StarCraft AI bots,” IEEE Conference on Computational Intelligence in Games (CIG), 2013, pp. 1-7, http://dx.doi.org/10.1109/CIG.2013.6633666.
• 7. G. Synnaeve and P. Bessière, “A Dataset for StarCraft AI & an Example of Armies Clustering,” Artificial Intelligence in Adversarial Real-Time Games, 2012, https://arxiv.org/abs/1211.4552.
• 8. Š. Krištofı́k, P. Malı́k, M. Kasáš, Š. Neupauer, “StarCraft agent strategic training on a large human versus human game replay dataset,” Federated Conference on Computer Science and Information Systems, FedCSIS 2020, 21, ACSIS, pp. 391–399, http://dx.doi.org/10.15439/2020F178.
• 9. M. Świechowski, “Game AI Competitions: Motivation for the Imitation Game-Playing Competition,” Federated Conference on Computer Science and Information Systems, FedCSIS 2020, 21, ACSIS, pp. 155–160, http://dx.doi.org/10.15439/2020F126.

Uwagi

1. Track 1: Artificial Intelligence in Applications

2. Session: 15th International Symposium Advances in Artificial Intelligence and Applications

3. Short Paper