Monte Carlo search applied to card selection in Magic: The Gathering

Ward, C. D.; Cowling, Peter I.

doi:10.1109/cig.2009.5286501

Cited by 40 publications

(27 citation statements)

References 15 publications

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“…In the Hearthstone reddit 8 , there is a report of a MCTS implementation for Hearthstone, though with some limitations such as having perfect knowledge. In the broader scope of TCGs, there is also work on using MCTS by Ward et al [6] for a simplified version of "Magic: The Gathering" only allowing minion cards and a greatly reduced card pool. In their work, they illustrate a common approach, the bandit-based MCTS implementing an Upper Confidence Bound (UCB or UCT if applied to trees).…”

Section: Monte Carlo Tree Searchmentioning

confidence: 99%

“…Due to these properties, TCGs are already used in academic work, such as using Monte Carlo Tree Search to cover uncertainty [5] or card selection [6]. Other work, such as HoningStone [7], focuses on the creative aspects of selecting card combos.…”

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confidence: 99%

“…As described in [1], all academic StarCraft bots in the respective challenges used a multi-agent structure of some kind, typically splitting the reasoning task into higher-level planning and lower-level micro-management problems. Churchill, one of the developers of the TCG and RTS hybrid "Prismata" 6 , dissects the architecture of their TCG-AI in [8] and also illustrates the challenges.…”

mentioning

confidence: 99%

“…Other common metrics for player-level AI evaluation, such as runtime performance and controllability, are secondary. This 5 Rio Grande Games, 2008 (http://riograndegames.com/Game/278-Dominion) 6 Lunarch Studios (http://blog.prismata.net/2014/12/17/the-prismata-ai/)…”

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confidence: 99%

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Symbolic Reasoning for <italic>Hearthstone</italic>

et al. 2018

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Abstract-Trading-Card-Games are an interesting problem domain for Game AI, as they feature some challenges, such as highly variable game mechanics, that are not encountered in this intensity in many other genres. We present an expert system forming a player-level AI for the digital Trading-Card-Game Hearthstone. The bot uses a symbolic approach with a semantic structure, acting as an ontology, to represent both static descriptions of the game mechanics and dynamic game-state memories. Methods are introduced to reduce the amount of expert knowledge, such as popular moves or strategies, represented in the ontology, as the bot should derive such decisions in a symbolic way from its knowledge base. We narrow down the problem domain, selecting the relevant aspects for a play-to-win bot approach and comparing an ontology-driven approach to other approaches such as machine learning and case-based reasoning. Upon this basis, we describe how the semantic structure is linked with the game-state and how different aspects, such as memories, are encoded. An example will illustrate how the bot, at runtime, uses rules and queries on the semantic structure combined with a simple utility system to do reasoning and strategic planning. Finally, an evaluation is presented that was conducted by fielding the bot against the stock "Expert" AI that Hearthstone is shipped with, as well as Human opponents of various skill levels in order to assess how well the bot plays. Evaluating how believable the bot reasons is assessed through a Pseudo-Turing test.

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Section: Monte Carlo Tree Searchmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Symbolic Reasoning for <italic>Hearthstone</italic>

et al. 2018

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“…The Gathering (Ward and Cowling, 2009;Cowling et al, 2012), a card game similar to Hearthstone. These approaches however usually solve subproblems of the game they are applied to, and not the game itself, through the abstraction of many variables.…”

Section: Machine Learning and Video Gamesmentioning

confidence: 99%

Decision Making from Confidence Measurement on the Reward Growth using Supervised Learning - A Study Intended for Large-scale Video Games

Taralla

Qiu

Sutera

et al. 2016

Proceedings of the 8th International Conference on Agents and Artificial Intelligence

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Abstract:Video games have become more and more complex over the past decades. Today, players wander in visuallyand option-rich environments, and each choice they make, at any given time, can have a combinatorial number of consequences. However, modern artificial intelligence is still usually hard-coded, and as the game environments become increasingly complex, this hard-coding becomes exponentially difficult. Recent research works started to let video game autonomous agents learn instead of being taught, which makes them more intelligent. This contribution falls under this very perspective, as it aims to develop a framework for the generic design of autonomous agents for large-scale video games. We consider a class of games for which expert knowledge is available to define a state quality function that gives how close an agent is from its objective. The decision making policy is based on a confidence measurement on the growth of the state quality function, computed by a supervised learning classification model. Additionally, no stratagems aiming to reduce the action space are used. As a proof of concept, we tested this simple approach on the collectible card game Hearthstone and obtained encouraging results.

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The Feasibility of Deep Counterfactual Regret Minimisation for Trading Card Games

Adams

2022

AI 2022: Advances in Artificial Intelligence

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Monte Carlo search applied to card selection in Magic: The Gathering

Cited by 40 publications

References 15 publications

Symbolic Reasoning for <italic>Hearthstone</italic>

Symbolic Reasoning for <italic>Hearthstone</italic>

Decision Making from Confidence Measurement on the Reward Growth using Supervised Learning - A Study Intended for Large-scale Video Games

The Feasibility of Deep Counterfactual Regret Minimisation for Trading Card Games

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