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WARDS: Modelling the Worth of Vision in MOBA’s

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1229)

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’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.

Keywords

Machine learning Dota 2 League of Legends Esports Neural networks Imperfect information game Information gathering Real time prediction 

Notes

Acknowledgments

This work has been created as part of the Weavr project (weavr.tv) and was funded within the Audience of the Future programme by UK Research and Innovation through the Industrial Strategy Challenge Fund (grant no. 104775) and supported by the Digital Creativity Labs (digitalcreativity.ac.uk), a jointly funded project by EPSRC/AHRC/Innovate UK under grant no. EP/M023265/1. We would also like to thank the “Ogreboy’s Free Coaching Serve” Discord server for agreeing to let us use their server as a platform and the following players for their input: Fyre, Water, Arzetlam, Trepo and Ogreboy.

Lastly we would like to thank Isabelle Noelle for enabling the timely delivery of this project.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Weavr, Arena Research Cluster (ARC)University of YorkYorkUK

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