Monte Carlo Tree Search Experiments in Hearthstone

André Santos; Pedro A. Santos; Francisco S. Melo

doi:10.1109/CIG.2017.8080446

Monte Carlo Tree Search Experiments in Hearthstone

André Santos, Pedro A. Santos, Francisco S. Melo

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

Abstract

In this paper, we introduce a Monte-Carlo tree search (MCTS) approach for the game “Hearthstone: Heroes of Warcraft”. We argue that, in light of the challenges posed by the game (such as uncertainty and hidden information), Monte Carlo tree search offers an appealing alternative to existing AI players. Additionally, by enriching MCTS with a properly constructed heuristic, it is possible to introduce significant gains in performance.We illustrate through extensive empirical validation the superior performance of our approach against vanilla MCTS and the current state-of-the art AI for Hearthstone.

Original language	English
Title of host publication	2017 IEEE Conference on Computational Intelligence and Games (CIG)
Place of Publication	New York, NY
Publisher	IEEE
Pages	272-279
ISBN (Electronic)	978-1-5386-3233-8
DOIs	https://doi.org/10.1109/CIG.2017.8080446
Publication status	Published - 2017
Externally published	Yes
Event	2017 Conference on Computational Intelligence in Games - New York University, New York, United States Duration: 22 Aug 2017 → 25 Aug 2017 http://www.cig2017.com/

Conference

Conference	2017 Conference on Computational Intelligence in Games
Abbreviated title	CiG2017
Country/Territory	United States
City	New York
Period	22/08/17 → 25/08/17
Internet address	http://www.cig2017.com/

Keywords

Monte Carlo Tree Search
Artificial intelligence for games
Hearthstone

Access to Document

10.1109/CIG.2017.8080446

Rage: Realising an Applied Gaming Eco-system
Westera, W. (PI), Georgiadis, K. (CoI), Saveski, G. (CoI), van Lankveld, G. (CoI), Bahreini, K. (CoI), van der Vegt, W. (CoI), Berkhout, J. (CoI), Nyamsuren, E. (CoI), Kluijfhout, E. (CoI) & Nadolski, R. (CoI)
European Union
1/02/15 → 31/07/19
Project: Research

Cite this

@inproceedings{e13a3c8af26e41b7b41f772b95e0a0b7,

title = "Monte Carlo Tree Search Experiments in Hearthstone",

abstract = "In this paper, we introduce a Monte-Carlo tree search (MCTS) approach for the game “Hearthstone: Heroes of Warcraft”. We argue that, in light of the challenges posed by the game (such as uncertainty and hidden information), Monte Carlo tree search offers an appealing alternative to existing AI players. Additionally, by enriching MCTS with a properly constructed heuristic, it is possible to introduce significant gains in performance.We illustrate through extensive empirical validation the superior performance of our approach against vanilla MCTS and the current state-of-the art AI for Hearthstone.",

keywords = "Monte Carlo Tree Search, Artificial intelligence for games, Hearthstone",

author = "Andr{\'e} Santos and Santos, {Pedro A.} and Melo, {Francisco S.}",

year = "2017",

doi = "10.1109/CIG.2017.8080446",

language = "English",

pages = "272--279",

booktitle = "2017 IEEE Conference on Computational Intelligence and Games (CIG)",

publisher = "IEEE",

address = "United States",

note = "2017 Conference on Computational Intelligence in Games, CiG2017 ; Conference date: 22-08-2017 Through 25-08-2017",

url = "http://www.cig2017.com/",

}

TY - GEN

T1 - Monte Carlo Tree Search Experiments in Hearthstone

AU - Santos, André

AU - Santos, Pedro A.

AU - Melo, Francisco S.

PY - 2017

Y1 - 2017

N2 - In this paper, we introduce a Monte-Carlo tree search (MCTS) approach for the game “Hearthstone: Heroes of Warcraft”. We argue that, in light of the challenges posed by the game (such as uncertainty and hidden information), Monte Carlo tree search offers an appealing alternative to existing AI players. Additionally, by enriching MCTS with a properly constructed heuristic, it is possible to introduce significant gains in performance.We illustrate through extensive empirical validation the superior performance of our approach against vanilla MCTS and the current state-of-the art AI for Hearthstone.

AB - In this paper, we introduce a Monte-Carlo tree search (MCTS) approach for the game “Hearthstone: Heroes of Warcraft”. We argue that, in light of the challenges posed by the game (such as uncertainty and hidden information), Monte Carlo tree search offers an appealing alternative to existing AI players. Additionally, by enriching MCTS with a properly constructed heuristic, it is possible to introduce significant gains in performance.We illustrate through extensive empirical validation the superior performance of our approach against vanilla MCTS and the current state-of-the art AI for Hearthstone.

KW - Monte Carlo Tree Search

KW - Artificial intelligence for games

KW - Hearthstone

U2 - 10.1109/CIG.2017.8080446

DO - 10.1109/CIG.2017.8080446

M3 - Conference Article in proceeding

SP - 272

EP - 279

BT - 2017 IEEE Conference on Computational Intelligence and Games (CIG)

PB - IEEE

CY - New York, NY

T2 - 2017 Conference on Computational Intelligence in Games

Y2 - 22 August 2017 through 25 August 2017

ER -

Monte Carlo Tree Search Experiments in Hearthstone

Abstract

Conference

Keywords

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Projects

Rage: Realising an Applied Gaming Eco-system

Cite this