Evolutionary deckbuilding in hearthstone

García‐Sánchez, Pablo; Tonda, Alberto; Squillero, Giovanni; Mora, Antonio M.; Merelo, J. J.

doi:10.1109/cig.2016.7860426

Cited by 29 publications

(28 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous work on evolutionary Hearthstone deckbuilding in particular employed non-QD evolutionary algorithms to generate better starter decks by evaluating decks against a single AI opponent [3]. García-Sánchez et al for Hearthstone [18,19] similarly evaluated evolved decks against a suite of opponents. Similar approaches proved successful for the deckbuilding game Magic: The Gathering [4].…”

Section: Automated Deckbuilding and Playtestingmentioning

confidence: 99%

Mapping hearthstone deck spaces through MAP-elites with sliding boundaries

Fontaine

Lee

Soros

et al. 2019

Proceedings of the Genetic and Evolutionary Computation Conference

View full text Add to dashboard Cite

Quality diversity (QD) algorithms such as MAP-Elites have emerged as a powerful alternative to traditional single-objective optimization methods. They were initially applied to evolutionary robotics problems such as locomotion and maze navigation, but have yet to see widespread application. We argue that these algorithms are perfectly suited to the rich domain of video games, which contains many relevant problems with a multitude of successful strategies and often also multiple dimensions along which solutions can vary.This paper introduces a novel modification of the MAP-Elites algorithm called MAP-Elites with Sliding Boundaries (MESB) and applies it to the design and rebalancing of Hearthstone, a popular collectible card game chosen for its number of multidimensional behavior features relevant to particular styles of play. To avoid overpopulating cells with conflated behaviors, MESB slides the boundaries of cells based on the distribution of evolved individuals. Experiments in this paper demonstrate the performance of MESB in Hearthstone. Results suggest MESB finds diverse ways of playing the game well along the selected behavioral dimensions. Further analysis of the evolved strategies reveals common patterns that recur across behavioral dimensions and explores how MESB can help rebalance the game.

show abstract

Section: Automated Deckbuilding and Playtestingmentioning

confidence: 99%

Mapping hearthstone deck spaces through MAP-elites with sliding boundaries

Fontaine

Lee

Soros

et al. 2019

Proceedings of the Genetic and Evolutionary Computation Conference

View full text Add to dashboard Cite

show abstract

“…While playing the game given an existing deck has so far received the most attention, there are interesting challenges inherent in the domain of building these decks [4,19,20,21]. Human players often build them through experimentation and the evolving meta strategies of expert players, but automatically creating such decks could potentially lead to a richer diversity of meta strategies.…”

Section: Building Decksmentioning

confidence: 99%

“…While there are some approaches to building decks compatible with a specific agent or playstyle [4,19,20,21], fewer approaches identify the most effective agent, playstyle, or strategy given a specific deck. This may be useful when players or agents must play with decks they have limited control over, such as Tavern Brawl.…”

Section: Deck Analysis: Identifying Cores Weaknesses and Strategymentioning

confidence: 99%

The Many AI Challenges of Hearthstone

Hoover

Togelius

Lee³

et al. 2019

Künstl Intell

View full text Add to dashboard Cite

Games have benchmarked AI methods since the inception of the field, with classic board games such as Chess and Go recently leaving room for video games with related yet different sets of challenges. The set of AI problems associated with video games has in recent decades expanded from simply playing games to win, to playing games in particular styles, generating game content, modeling players etc. Different games pose very different challenges for AI systems, and several different AI challenges can typically be posed by the same game. In this article we analyze the popular collectible card game Hearthstone (Blizzard 2014) and describe a varied set of interesting AI challenges posed by this game. Collectible card games are relatively understudied in the AI community, despite their popularity and the interesting challenges they pose. Analyzing a single game in-depth in the manner we do here allows us to see the entire field of AI and Games through the lens of a single game, discovering a few new variations on existing research topics.

show abstract

“…Several groups of researchers from the field of machine learning and AI have already chosen Hearthstone for their studies. In [1], authors used evolutionary algorithms to tackle the problem of building good decks. They used the results of simulated games performed by simple AI bots as fitness function values.…”

Section: Related Workmentioning

confidence: 99%

“…In our work, however, we focus only on Hearthstone. The growing interest of the machine learning community in applications related to video games stems from the fact that solutions to many game-related problems could be 1 Competition's web page: https://knowledgepit.fedcsis.org/contest/view.php?id=120 easily transfered to real-life issues, such as planning [11], realtime decision making [12], [13] and, ultimately, general AI.…”

Section: Related Workmentioning

confidence: 99%

Improving Hearthstone AI by Combining MCTS and Supervised Learning Algorithms

Świechowski¹,

Tajmajer²,

Janusz³

2018

2018 IEEE Conference on Computational Intelligence and Games (CIG)

View full text Add to dashboard Cite

Warning: this is not the final camera-ready version. Abstract-We investigate the impact of supervised prediction models on the strength and efficiency of artificial agents that use the Monte-Carlo Tree Search (MCTS) algorithm to play a popular video game Hearthstone: Heroes of Warcraft. We overview our custom implementation of the MCTS that is well-suited for games with partially hidden information and random effects. We also describe experiments which we designed to quantify the performance of our Hearthstone agent's decision making. We show that even simple neural networks can be trained and successfully used for the evaluation of game states. Moreover, we demonstrate that by providing a guidance to the game state search heuristic, it is possible to substantially improve the win rate, and at the same time reduce the required computations.

show abstract

Evolutionary deckbuilding in hearthstone

Cited by 29 publications

References 10 publications

Mapping hearthstone deck spaces through MAP-elites with sliding boundaries

Mapping hearthstone deck spaces through MAP-elites with sliding boundaries

The Many AI Challenges of Hearthstone

Improving Hearthstone AI by Combining MCTS and Supervised Learning Algorithms

Contact Info

Product

Resources

About