Toward an Intelligent HS Deck Advisor: Lessons Learned from AAIA'18 Data Mining Competition

Janusz, Andrzej; Tajmajer, Tomasz; Grad, Lukasz; Puczniewski, Jacek; Ślęzak, Dominik

doi:10.15439/2018f386

Cited by 12 publications

(9 citation statements)

References 9 publications

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“…The performance of players in Hearthstone is influenced by the decks they are using. For the experiments, we included four various types of decks that have also been used in the Hearthstone-based data mining competition [31]:…”

Section: B Experimental Setupmentioning

confidence: 99%

A Practical Solution to Handling Randomness and Imperfect Information in Monte Carlo Tree Search

Tajmajer

2021

Annals of Computer Science and Information Systems

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This paper provides practical guidelines for developing strong AI agents based on the Monte Carlo Tree Search algorithm in a game with imperfect information and/or randomness. These guidelines are backed up by series of experiments carried out in the very popular game -Hearthstone. Despite the focus on Hearthstone, the paper is written with reusability and universal applications in mind. For MCTS algorithm, we introduced a few novel ideas such as complete elimination of the so-called nature moves, separation of decision and simulation states as well as a multi-layered transposition table. These have helped to create a strong Hearthstone agent.

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Section: B Experimental Setupmentioning

confidence: 99%

A Practical Solution to Handling Randomness and Imperfect Information in Monte Carlo Tree Search

Tajmajer

2021

Annals of Computer Science and Information Systems

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“…The second one, has even featured game-based competition e.g. aimed at advising players [42] in card games. The second option, suitable for competitions with lesser amounts of entries, would be to have human referees judging the bots as shown in the Generative Design in Minecraft Competition.…”

Section: A Descriptionmentioning

confidence: 99%

Game AI Competitions: Motivation for the Imitation Game-Playing Competition

Świechowski

2020

Proceedings of the 2020 Federated Conference on Computer Science and Information Systems

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Games have played crucial role in advancing research in Artificial Intelligence and tracking its progress. In this article, a new proposal for game AI competition is presented. The goal is to create computer players which can learn and mimic the behavior of particular human players given access to their game records. We motivate usefulness of such an approach in various aspects, e.g., new ways of understanding what constitutes the human-like AI or how well it fits into the existing game production workflows. This competition may integrate many problems such as learning, representation, approximation and compression of AI, pattern recognition, knowledge extraction etc. This leads to multi-directional implications both on research and industry. In addition to the proposal, we include a short survey of the available game AI competitions.

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“…As a result, there are many approaches to creating AI agents to play Hearthstone [11,16,20,39,40,42,45]. Furthermore, there have been significant advancements in win predictions based on game state evaluation [26,28,29]. Bursztein [6] took a unique approach by building a predictor to determine the next card that the opponent is likely to play.…”

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

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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.

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Toward an Intelligent HS Deck Advisor: Lessons Learned from AAIA'18 Data Mining Competition

Cited by 12 publications

References 9 publications

A Practical Solution to Handling Randomness and Imperfect Information in Monte Carlo Tree Search

A Practical Solution to Handling Randomness and Imperfect Information in Monte Carlo Tree Search

Game AI Competitions: Motivation for the Imitation Game-Playing Competition

Mapping hearthstone deck spaces through MAP-elites with sliding boundaries

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