Improving StarCraft II Player League Prediction with Macro-Level Features

Yinheng Chen; Matthew Aitchison; Penny Sweetser

doi:10.1007/978-3-030-64984-5_20

Improving StarCraft II Player League Prediction with Macro-Level Features

Yinheng Chen, Matthew Aitchison, Penny Sweetser^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

Accurate player skill modelling is an important but challenging task in Real-Time Strategy Games. Previous efforts have relied strongly on micromanagement features, such as Actions Per Minute, producing limited results. In this paper, we present an improved player skill classifier for StarCraft II that predicts, from a replay, a player’s exact league at 61.7% accuracy, or within one league at 94.5%, outperforming the previous state of the art of 47.3%. Unlike previous classifiers, our classifier makes use of a macro-level measure of economic performance, called Spending Quotient, which we demonstrate to be an important part of accurately predicting player skill levels.

Original language	English
Title of host publication	AI 2020
Subtitle of host publication	Advances in Artificial Intelligence - 33rd Australasian Joint Conference, AI 2020, Proceedings
Editors	Marcus Gallagher, Nour Moustafa, Erandi Lakshika
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	256-268
Number of pages	13
ISBN (Print)	9783030649838
DOIs	https://doi.org/10.1007/978-3-030-64984-5_20
Publication status	Published - 2020
Event	33rd Australasian Joint Conference on Artificial Intelligence, AI 2020 - Canberra, ACT, Australia Duration: 29 Nov 2020 → 30 Nov 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12576 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	33rd Australasian Joint Conference on Artificial Intelligence, AI 2020
Country/Territory	Australia
City	Canberra, ACT
Period	29/11/20 → 30/11/20

Access to Document

10.1007/978-3-030-64984-5_20

Cite this

Chen, Y., Aitchison, M., & Sweetser, P. (2020). Improving StarCraft II Player League Prediction with Macro-Level Features. In M. Gallagher, N. Moustafa, & E. Lakshika (Eds.), AI 2020: Advances in Artificial Intelligence - 33rd Australasian Joint Conference, AI 2020, Proceedings (pp. 256-268). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12576 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-64984-5_20

Chen, Yinheng ; Aitchison, Matthew ; Sweetser, Penny. / Improving StarCraft II Player League Prediction with Macro-Level Features. AI 2020: Advances in Artificial Intelligence - 33rd Australasian Joint Conference, AI 2020, Proceedings. editor / Marcus Gallagher ; Nour Moustafa ; Erandi Lakshika. Springer Science and Business Media Deutschland GmbH, 2020. pp. 256-268 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Accurate player skill modelling is an important but challenging task in Real-Time Strategy Games. Previous efforts have relied strongly on micromanagement features, such as Actions Per Minute, producing limited results. In this paper, we present an improved player skill classifier for StarCraft II that predicts, from a replay, a player{\textquoteright}s exact league at 61.7% accuracy, or within one league at 94.5%, outperforming the previous state of the art of 47.3%. Unlike previous classifiers, our classifier makes use of a macro-level measure of economic performance, called Spending Quotient, which we demonstrate to be an important part of accurately predicting player skill levels.",

keywords = "Machine learning, Player modeling, Real-Time Strategy Games",

author = "Yinheng Chen and Matthew Aitchison and Penny Sweetser",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 33rd Australasian Joint Conference on Artificial Intelligence, AI 2020 ; Conference date: 29-11-2020 Through 30-11-2020",

year = "2020",

doi = "10.1007/978-3-030-64984-5_20",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Chen, Y, Aitchison, M & Sweetser, P 2020, Improving StarCraft II Player League Prediction with Macro-Level Features. in M Gallagher, N Moustafa & E Lakshika (eds), AI 2020: Advances in Artificial Intelligence - 33rd Australasian Joint Conference, AI 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12576 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 256-268, 33rd Australasian Joint Conference on Artificial Intelligence, AI 2020, Canberra, ACT, Australia, 29/11/20. https://doi.org/10.1007/978-3-030-64984-5_20

Improving StarCraft II Player League Prediction with Macro-Level Features. / Chen, Yinheng; Aitchison, Matthew; Sweetser, Penny.
AI 2020: Advances in Artificial Intelligence - 33rd Australasian Joint Conference, AI 2020, Proceedings. ed. / Marcus Gallagher; Nour Moustafa; Erandi Lakshika. Springer Science and Business Media Deutschland GmbH, 2020. p. 256-268 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12576 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Improving StarCraft II Player League Prediction with Macro-Level Features

AU - Chen, Yinheng

AU - Aitchison, Matthew

AU - Sweetser, Penny

PY - 2020

Y1 - 2020

N2 - Accurate player skill modelling is an important but challenging task in Real-Time Strategy Games. Previous efforts have relied strongly on micromanagement features, such as Actions Per Minute, producing limited results. In this paper, we present an improved player skill classifier for StarCraft II that predicts, from a replay, a player’s exact league at 61.7% accuracy, or within one league at 94.5%, outperforming the previous state of the art of 47.3%. Unlike previous classifiers, our classifier makes use of a macro-level measure of economic performance, called Spending Quotient, which we demonstrate to be an important part of accurately predicting player skill levels.

AB - Accurate player skill modelling is an important but challenging task in Real-Time Strategy Games. Previous efforts have relied strongly on micromanagement features, such as Actions Per Minute, producing limited results. In this paper, we present an improved player skill classifier for StarCraft II that predicts, from a replay, a player’s exact league at 61.7% accuracy, or within one league at 94.5%, outperforming the previous state of the art of 47.3%. Unlike previous classifiers, our classifier makes use of a macro-level measure of economic performance, called Spending Quotient, which we demonstrate to be an important part of accurately predicting player skill levels.

KW - Machine learning

KW - Player modeling

KW - Real-Time Strategy Games

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U2 - 10.1007/978-3-030-64984-5_20

DO - 10.1007/978-3-030-64984-5_20

M3 - Conference contribution

SN - 9783030649838

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 256

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BT - AI 2020

A2 - Gallagher, Marcus

A2 - Moustafa, Nour

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PB - Springer Science and Business Media Deutschland GmbH

T2 - 33rd Australasian Joint Conference on Artificial Intelligence, AI 2020

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Chen Y, Aitchison M, Sweetser P. Improving StarCraft II Player League Prediction with Macro-Level Features. In Gallagher M, Moustafa N, Lakshika E, editors, AI 2020: Advances in Artificial Intelligence - 33rd Australasian Joint Conference, AI 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 256-268. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-64984-5_20

Improving StarCraft II Player League Prediction with Macro-Level Features

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