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 -