By the same authors

WARDS: Modelling the Worth of Vision in MOBA’s

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

Standard

WARDS : Modelling the Worth of Vision in MOBA’s. / Pedrassoli Chitayat, Alan; Kokkinakis, Athanasios; Patra, Sagarika; Demediuk, Simon; Robertson, Justus; Olarewaju, Oluseji; Ursu, Marian; Kirman, Ben; Hook, Jonathan; Block, Florian; Drachen, Anders.

Intelligent Computing - Proceedings of the 2020 Computing Conference. ed. / Kohei Arai; Supriya Kapoor; Rahul Bhatia. Springer, 2020. p. 63-81 (Advances in Intelligent Systems and Computing; Vol. 1229 AISC).

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

Harvard

Pedrassoli Chitayat, A, Kokkinakis, A, Patra, S, Demediuk, S, Robertson, J, Olarewaju, O, Ursu, M, Kirman, B, Hook, J, Block, F & Drachen, A 2020, WARDS: Modelling the Worth of Vision in MOBA’s. in K Arai, S Kapoor & R Bhatia (eds), Intelligent Computing - Proceedings of the 2020 Computing Conference. Advances in Intelligent Systems and Computing, vol. 1229 AISC, Springer, pp. 63-81, Science and Information Conference, SAI 2020, London, United Kingdom, 16/07/20. https://doi.org/10.1007/978-3-030-52246-9_5

APA

Pedrassoli Chitayat, A., Kokkinakis, A., Patra, S., Demediuk, S., Robertson, J., Olarewaju, O., Ursu, M., Kirman, B., Hook, J., Block, F., & Drachen, A. (2020). WARDS: Modelling the Worth of Vision in MOBA’s. In K. Arai, S. Kapoor, & R. Bhatia (Eds.), Intelligent Computing - Proceedings of the 2020 Computing Conference (pp. 63-81). (Advances in Intelligent Systems and Computing; Vol. 1229 AISC). Springer. https://doi.org/10.1007/978-3-030-52246-9_5

Vancouver

Pedrassoli Chitayat A, Kokkinakis A, Patra S, Demediuk S, Robertson J, Olarewaju O et al. WARDS: Modelling the Worth of Vision in MOBA’s. In Arai K, Kapoor S, Bhatia R, editors, Intelligent Computing - Proceedings of the 2020 Computing Conference. Springer. 2020. p. 63-81. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-030-52246-9_5

Author

Pedrassoli Chitayat, Alan ; Kokkinakis, Athanasios ; Patra, Sagarika ; Demediuk, Simon ; Robertson, Justus ; Olarewaju, Oluseji ; Ursu, Marian ; Kirman, Ben ; Hook, Jonathan ; Block, Florian ; Drachen, Anders. / WARDS : Modelling the Worth of Vision in MOBA’s. Intelligent Computing - Proceedings of the 2020 Computing Conference. editor / Kohei Arai ; Supriya Kapoor ; Rahul Bhatia. Springer, 2020. pp. 63-81 (Advances in Intelligent Systems and Computing).

Bibtex - Download

@inproceedings{7aad896d3f8d40c489098202b0b3b50c,
title = "WARDS: Modelling the Worth of Vision in MOBA{\textquoteright}s",
abstract = "Multiplayer strategy games are examples of imperfect information games, where information about the game state can be retrieved through in-game mechanics. One such mechanic is vision. Within esports titles of this genre, such as League of Legends (LoL) and Dota 2, players often gather map information through the use of friendly units called wards. In LoL, one of the most popular esports title worldwide, warding has hitherto been evaluated only using a heuristic called vision score, provided by Riot, the game{\textquoteright}s developer. In this paper, we examine the accuracy at LoL{\textquoteright}s vision score at predicting the overall game-winner within the context supported by the game. We have ported LoL{\textquoteright}s vision score to Dota 2, a similarly popular esports title, and compared its performance against a novel warding model. We have compared both models not only at predicting the overall winner, but also the current state of the game and their ability to predict and reflect short term game advantage and events. We found our model significantly outperformed LoL{\textquoteright}s vision score. Additionally, we trained and evaluated a model for predicting the value of wards in real-time through the use of a Neural Network.",
keywords = "Dota 2, Esports, Imperfect information game, Information gathering, League of Legends, Machine learning, Neural networks, Real time prediction",
author = "{Pedrassoli Chitayat}, Alan and Athanasios Kokkinakis and Sagarika Patra and Simon Demediuk and Justus Robertson and Oluseji Olarewaju and Marian Ursu and Ben Kirman and Jonathan Hook and Florian Block and Anders Drachen",
note = "This is an author-produced version of the published paper. Uploaded in accordance with the publisher{\textquoteright}s self-archiving policy. Further copying may not be permitted; contact the publisher for details.; Science and Information Conference, SAI 2020 ; Conference date: 16-07-2020 Through 17-07-2020",
year = "2020",
month = jul,
day = "4",
doi = "10.1007/978-3-030-52246-9_5",
language = "English",
isbn = "9783030522452",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer",
pages = "63--81",
editor = "Kohei Arai and Supriya Kapoor and Rahul Bhatia",
booktitle = "Intelligent Computing - Proceedings of the 2020 Computing Conference",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - WARDS

T2 - Science and Information Conference, SAI 2020

AU - Pedrassoli Chitayat, Alan

AU - Kokkinakis, Athanasios

AU - Patra, Sagarika

AU - Demediuk, Simon

AU - Robertson, Justus

AU - Olarewaju, Oluseji

AU - Ursu, Marian

AU - Kirman, Ben

AU - Hook, Jonathan

AU - Block, Florian

AU - Drachen, Anders

N1 - This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.

PY - 2020/7/4

Y1 - 2020/7/4

N2 - Multiplayer strategy games are examples of imperfect information games, where information about the game state can be retrieved through in-game mechanics. One such mechanic is vision. Within esports titles of this genre, such as League of Legends (LoL) and Dota 2, players often gather map information through the use of friendly units called wards. In LoL, one of the most popular esports title worldwide, warding has hitherto been evaluated only using a heuristic called vision score, provided by Riot, the game’s developer. In this paper, we examine the accuracy at LoL’s vision score at predicting the overall game-winner within the context supported by the game. We have ported LoL’s vision score to Dota 2, a similarly popular esports title, and compared its performance against a novel warding model. We have compared both models not only at predicting the overall winner, but also the current state of the game and their ability to predict and reflect short term game advantage and events. We found our model significantly outperformed LoL’s vision score. Additionally, we trained and evaluated a model for predicting the value of wards in real-time through the use of a Neural Network.

AB - Multiplayer strategy games are examples of imperfect information games, where information about the game state can be retrieved through in-game mechanics. One such mechanic is vision. Within esports titles of this genre, such as League of Legends (LoL) and Dota 2, players often gather map information through the use of friendly units called wards. In LoL, one of the most popular esports title worldwide, warding has hitherto been evaluated only using a heuristic called vision score, provided by Riot, the game’s developer. In this paper, we examine the accuracy at LoL’s vision score at predicting the overall game-winner within the context supported by the game. We have ported LoL’s vision score to Dota 2, a similarly popular esports title, and compared its performance against a novel warding model. We have compared both models not only at predicting the overall winner, but also the current state of the game and their ability to predict and reflect short term game advantage and events. We found our model significantly outperformed LoL’s vision score. Additionally, we trained and evaluated a model for predicting the value of wards in real-time through the use of a Neural Network.

KW - Dota 2

KW - Esports

KW - Imperfect information game

KW - Information gathering

KW - League of Legends

KW - Machine learning

KW - Neural networks

KW - Real time prediction

UR - http://www.scopus.com/inward/record.url?scp=85088497655&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-52246-9_5

DO - 10.1007/978-3-030-52246-9_5

M3 - Conference contribution

AN - SCOPUS:85088497655

SN - 9783030522452

T3 - Advances in Intelligent Systems and Computing

SP - 63

EP - 81

BT - Intelligent Computing - Proceedings of the 2020 Computing Conference

A2 - Arai, Kohei

A2 - Kapoor, Supriya

A2 - Bhatia, Rahul

PB - Springer

Y2 - 16 July 2020 through 17 July 2020

ER -