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README.md
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@ -31,6 +31,7 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub](
- [Game Design](#game-design)
- [General Gameplay](#general-gameplay)
- [Hanabi](#hanabi)
- [Hearthstone](#hearthstone)
- [Hive](#hive)
- [Jenga](#jenga)
- [Kingdomino](#kingdomino)
@ -41,6 +42,7 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub](
- [Modern Art: The card game](#modern-art-the-card-game)
- [Monopoly](#monopoly)
- [Monopoly Deal](#monopoly-deal)
- [Netrunner](#netrunner)
- [Nmbr9](#nmbr9)
- [Pandemic](#pandemic)
- [Patchwork](#patchwork)
@ -161,6 +163,9 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub](
- [A Graphical User Interface For The Hanabi Challenge Benchmark](http://oru.diva-portal.org/smash/record.jsf?pid=diva2%3A1597503) (thesis)
- [Emergence of Cooperative Impression With Self-Estimation, Thinking Time, and Concordance of Risk Sensitivity in Playing Hanabi](https://www.frontiersin.org/articles/10.3389/frobt.2021.658348/full) (journalArticle)
# Hearthstone
- [Mapping Hearthstone Deck Spaces through MAP-Elites with Sliding Boundaries](http://arxiv.org/abs/1904.10656) (journalArticle)
# Hive
- [On the complexity of Hive](https://dspace.library.uu.nl/handle/1874/396955) (thesis)
@ -197,6 +202,7 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub](
- [Deck Construction Strategies for Magic: The Gathering](https://www.doi.org/10.1685/CSC06077) (conferencePaper)
- [Deckbuilding in Magic: The Gathering Using a Genetic Algorithm](https://doi.org/11250/2462429) (thesis)
- [Magic: The Gathering Deck Performance Prediction](http://cs229.stanford.edu/proj2012/HauPlotkinTran-MagicTheGatheringDeckPerformancePrediction.pdf) (report)
- [AI solutions for drafting in Magic: the Gathering](http://arxiv.org/abs/2009.00655) (journalArticle)
# Mobile Games
- [Trainyard is NP-Hard](http://arxiv.org/abs/1603.00928v1) (journalArticle)
@ -220,6 +226,9 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub](
# Monopoly Deal
- [Implementation of Artificial Intelligence with 3 Different Characters of AI Player on “Monopoly Deal” Computer Game](https://doi.org/10.1007%2F978-3-662-46742-8_11) (bookSection)
# Netrunner
- [Netrunner Mate-in-1 or -2 is Weakly NP-Hard](http://arxiv.org/abs/1710.05121) (journalArticle)
# Nmbr9
- [Nmbr9 as a Constraint Programming Challenge](http://arxiv.org/abs/2001.04238) (journalArticle)
- [Nmbr9 as a Constraint Programming Challenge](https://zayenz.se/blog/post/nmbr9-cp2019-abstract/) (blogPost)
@ -234,6 +243,9 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub](
# Pentago
- [On Solving Pentago](http://www.ke.tu-darmstadt.de/lehre/arbeiten/bachelor/2011/Buescher_Niklas.pdf) (thesis)
- [Pentago is a First Player Win: Strongly Solving a Game Using Parallel In-Core Retrograde Analysis](http://arxiv.org/abs/1404.0743) (journalArticle)
- [A massively parallel pentago solver](https://github.com/girving/pentago) (computerProgram)
- [An interactive explorer for perfect pentago play](https://perfect-pentago.net/) (computerProgram)
# Quixo
- [QUIXO is EXPTIME-complete](https://doi.org/10.1016%2Fj.ipl.2020.105995) (journalArticle)
@ -241,6 +253,7 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub](
# Race for the Galaxy
- [SCOUT: A Case-Based Reasoning Agent for Playing Race for the Galaxy](https://doi.org/10.1007%2F978-3-319-61030-6_27) (bookSection)
- [Ludometrics: Luck, and How to Measure It](http://arxiv.org/abs/1811.00673) (journalArticle)
# Resistance: Avalon
- [Finding Friend and Foe in Multi-Agent Games](http://arxiv.org/abs/1906.02330) (journalArticle)
@ -287,6 +300,7 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub](
- [Playing Catan with Cross-dimensional Neural Network](http://arxiv.org/abs/2008.07079) (journalArticle)
- [Strategic Dialogue Management via Deep Reinforcement Learning](http://arxiv.org/abs/1511.08099) (journalArticle)
- [Strategic Dialogue Management via Deep Reinforcement Learning](http://arxiv.org/abs/1511.08099) (journalArticle)
- [Analysis of 'The Settlers of Catan' Using Markov Chains](https://repository.tcu.edu/handle/116099117/49062) (thesis)
# Shobu
- [Shobu AI Playground](https://github.com/JayWalker512/Shobu) (computerProgram)
@ -294,6 +308,8 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub](
# Terra Mystica
- [Using Tabu Search Algorithm for Map Generation in the Terra Mystica Tabletop Game](https://doi.org/10.1145%2F3396474.3396492) (conferencePaper)
- [Mastering Terra Mystica: Applying Self-Play to Multi-agent Cooperative Board Games](http://arxiv.org/abs/2102.10540) (journalArticle)
- [TM AI: Play TM against AI players.](https://lodev.org/tmai/) (computerProgram)
# Tetris Link
- [A New Challenge: Approaching Tetris Link with AI](http://arxiv.org/abs/2004.00377) (journalArticle)

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<dcterms:abstract>In this paper, we explore and compare multiple algorithms for solving the complex strategy game of Terra Mystica, hereafter abbreviated as TM. Previous work in the area of super-human game-play using AI has proven effective, with recent break-through for generic algorithms in games such as Go, Chess, and Shogi \cite{AlphaZero}. We directly apply these breakthroughs to a novel state-representation of TM with the goal of creating an AI that will rival human players. Specifically, we present the initial results of applying AlphaZero to this state-representation and analyze the strategies developed. A brief analysis is presented. We call this modified algorithm with our novel state-representation AlphaTM. In the end, we discuss the success and shortcomings of this method by comparing against multiple baselines and typical human scores. All code used for this paper is available at on \href{https://github.com/kandluis/terrazero}{GitHub}.</dcterms:abstract>
<dc:date>2021-02-21</dc:date>
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<dc:title>Netrunner Mate-in-1 or -2 is Weakly NP-Hard</dc:title>
<dcterms:abstract>We prove that deciding whether the Runner can win this turn (mate-in-1) in the Netrunner card game generalized to allow decks to contain an arbitrary number of copies of a card is weakly NP-hard. We also prove that deciding whether the Corp can win within two turns (mate-in-2) in this generalized Netrunner is weakly NP-hard.</dcterms:abstract>
<dc:date>2017-10-13</dc:date>
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<dc:title>Mapping Hearthstone Deck Spaces through MAP-Elites with Sliding Boundaries</dc:title>
<dcterms:abstract>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.</dcterms:abstract>
<dc:date>2019-04-24</dc:date>
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