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Hearthstone AI: Oops to Well Played

Published: 18 April 2019 Publication History

Abstract

Online digital collectible card games have seen a massive rise in popularity recently, none more so than Hearthstone: Heroes of Warcraft. While the game is mainly player vs. player focused, a need for competent game playing AI has arisen as well. This project attempts to tackle this problem by presenting a solution for a game playing AI using a tree-based game simulation and machine learning state evaluator. Additionally, the paper provides a way to breakdown Hearthstone into a feature set usable by machine learning algorithms. The AI based on this solution is shown to perform better than multiple testing solutions when played against them.

References

[1]
Hearthstone, https://playhearthstone.com/en-us.
[2]
Esport earning, https://www.esportsearnings.com/games/328-hearthstone.
[3]
A. Janusz, T. Tajmajer, and M. Swiechowski, "Helping AI to Play Hearthstone: AAIA'17 Data Mining Challenge", 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), Prague, 2017.
[4]
H. Teo, Y. Wang, J. Zhu, "Will our new Robot Overlords Play Hearthstone with Us?,", Stanford University, http://cs229.stanford.edu/proj2016spr/report/037.pdf, 2016.
[5]
Sabberstone, https://github.com/HearthSim/SabberStone.
[6]
Accord .NET, http://accord-framework.net.
[7]
HSReplay, https://hsreplay.net

Cited By

View all
  • (2024) CreativeStone: A Creativity Booster for Hearthstone Card Decks IEEE Transactions on Games10.1109/TG.2023.325814916:1(214-224)Online publication date: Mar-2024
  • (2022)Exploring Deep Reinforcement Learning for Battling in Collectible Card Games2022 21st Brazilian Symposium on Computer Games and Digital Entertainment (SBGames)10.1109/SBGAMES56371.2022.9961110(1-6)Online publication date: 24-Oct-2022
  • (2020)Drafting in Collectible Card Games via Reinforcement Learning2020 19th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames)10.1109/SBGames51465.2020.00018(54-61)Online publication date: Nov-2020

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Published In

cover image ACM Conferences
ACMSE '19: Proceedings of the 2019 ACM Southeast Conference
April 2019
295 pages
ISBN:9781450362511
DOI:10.1145/3299815
  • Conference Chair:
  • Dan Lo,
  • Program Chair:
  • Donghyun Kim,
  • Publications Chair:
  • Eric Gamess
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 April 2019

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Author Tags

  1. Hearthstone
  2. artificial intelligence
  3. feature
  4. game playing
  5. machine learning
  6. monte-carlo tree search
  7. neural network
  8. supervised learning

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  • Research-article
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  • Refereed limited

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ACM SE '19
Sponsor:
ACM SE '19: 2019 ACM Southeast Conference
April 18 - 20, 2019
GA, Kennesaw, USA

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Overall Acceptance Rate 502 of 1,023 submissions, 49%

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Cited By

View all
  • (2024) CreativeStone: A Creativity Booster for Hearthstone Card Decks IEEE Transactions on Games10.1109/TG.2023.325814916:1(214-224)Online publication date: Mar-2024
  • (2022)Exploring Deep Reinforcement Learning for Battling in Collectible Card Games2022 21st Brazilian Symposium on Computer Games and Digital Entertainment (SBGames)10.1109/SBGAMES56371.2022.9961110(1-6)Online publication date: 24-Oct-2022
  • (2020)Drafting in Collectible Card Games via Reinforcement Learning2020 19th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames)10.1109/SBGames51465.2020.00018(54-61)Online publication date: Nov-2020

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