New hearthstone research

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@ -195,6 +195,16 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub](
# Hearthstone
- [Mapping Hearthstone Deck Spaces through MAP-Elites with Sliding Boundaries](http://arxiv.org/abs/1904.10656) (journalArticle)
- [Multiplayer Tension In the Wild: A Hearthstone Case](https://research.tilburguniversity.edu/en/publications/multiplayer-tension-in-the-wild-a-hearthstone-case) (conferencePaper)
- [Improving Hearthstone AI by Combining MCTS and Supervised Learning Algorithms](http://arxiv.org/abs/1808.04794) (preprint)
- [Analysis of gameplay strategies in hearthstone: a data science approach](http://archives.njit.edu/vol01/etd/2020s/2020/njit-etd2020-006/njit-etd2020-006.pdf) (thesis)
- [Decision-Making in Hearthstone Based on Evolutionary Algorithm](https://www.cs.tsukuba.ac.jp/~hasebe/downloads/icaart2023_sakurai.pdf) (journalArticle)
- [I am a legend: Hacking hearthstone using statistical learning methods](http://ieeexplore.ieee.org/document/7860416/) (conferencePaper)
- [The Many AI Challenges of Hearthstone](http://link.springer.com/10.1007/s13218-019-00615-z) (journalArticle)
- [Evolutionary deckbuilding in hearthstone](http://ieeexplore.ieee.org/document/7860426/) (conferencePaper)
- [Evolving the Hearthstone Meta](http://arxiv.org/abs/1907.01623) (preprint)
- [Optimizing Hearthstone agents using an evolutionary algorithm](https://linkinghub.elsevier.com/retrieve/pii/S0950705119304356) (journalArticle)
- [Exploring the hearthstone deck space](https://dl.acm.org/doi/10.1145/3235765.3235791) (conferencePaper)
# Hive
- [On the complexity of Hive](https://dspace.library.uu.nl/handle/1874/396955) (thesis)

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@ -10966,6 +10966,630 @@ as the obstacles we encountered using each model.</dcterms:abstract>
DOI: 10.1007/978-3-319-59394-4_8</dc:description>
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<dcterms:abstract>Games are designed to elicit strong emotions during game play, especially when players are competing against each other. Artificial Intelligence applied to predict a player's emotions has mainly been tested on single-player experiences in low-stakes settings and short-term interactions. How do players experience and manifest affect in high-stakes competitions, and which modalities can capture this? This paper reports a first experiment in this line of research, using a competition of the video game Hearthstone where both competing players' game play and facial expressions were recorded over the course of the entire match which could span up to 41 minutes. Using two experts' annotations of tension using a continuous video affect annotation tool, we attempt to predict tension from the webcam footage of the players alone. Treating both the input and the tension output in a relative fashion, our best models reach 66.3% average accuracy (up to 79.2% at the best fold) in the challenging leave-one-participant out cross-validation task. This initial experiment shows a way forward for affect annotation in games {&quot;}in the wild{&quot;} in high-stakes, real-world competitive settings.</dcterms:abstract>
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<dcterms:abstract>In recent years, games have been a popular test bed for AI research, and the presence
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game where players seeks to implement one of several gameplay strategies to defeat
their opponent and decrease all of their Health points to zero. Although some
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opponents with a relatively low skill level. Using evolutionary algorithms, this thesis
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<dcterms:abstract>Hearthstone is a two-player turn-based collectible card game with hidden information and randomness. Gen-
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of actions by selecting each action from many options in each turn. When playing such a game, it is often
difficult to use a game tree search technique to find the optimal sequence of actions up until the end of a turn.
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mented an agent based on the proposed method and played it against an agent based on the original RHEA for
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the top-performing agents in previous competitions and outperformed most of them.</dcterms:abstract>
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<dcterms:abstract>Balancing an ever growing strategic game of high complexity, such as Hearthstone is a complex task. The target of making strategies diverse and customizable results in a delicate intricate system. Tuning over 2000 cards to generate the desired outcome without disrupting the existing environment becomes a laborious challenge. In this paper, we discuss the impacts that changes to existing cards can have on strategy in Hearthstone. By analyzing the win rate on match-ups across different decks, being played by different strategies, we propose to compare their performance before and after changes are made to improve or worsen different cards. Then, using an evolutionary algorithm, we search for a combination of changes to the card attributes that cause the decks to approach equal, 50% win rates. We then expand our evolutionary algorithm to a multi-objective solution to search for this result, while making the minimum amount of changes, and as a consequence disruption, to the existing cards. Lastly, we propose and evaluate metrics to serve as heuristics with which to decide which cards to target with balance changes.</dcterms:abstract>
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