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Adds Yahtzee links

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README.md
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@ -27,6 +27,7 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub](
- [Diplomacy](#diplomacy)
- [Dixit](#dixit)
- [Dominion](#dominion)
- [Game Design](#game-design)
- [Hanabi](#hanabi)
- [Hive](#hive)
- [Jenga](#jenga)
@ -35,7 +36,7 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub](
- [Mafia](#mafia)
- [Magic: The Gathering](#magic-the-gathering)
- [Mobile Games](#mobile-games)
- [Modern Art](#modern-art)
- [Modern Art: The card game](#modern-art-the-card-game)
- [Monopoly](#monopoly)
- [Monopoly Deal](#monopoly-deal)
- [Nmbr9](#nmbr9)
@ -44,6 +45,7 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub](
- [Pentago](#pentago)
- [Quixo](#quixo)
- [Race for the Galaxy](#race-for-the-galaxy)
- [Resistance: Avalon](#resistance-avalon)
- [RISK](#risk)
- [Santorini](#santorini)
- [Scotland Yard](#scotland-yard)
@ -53,7 +55,6 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub](
- [Shobu](#shobu)
- [Terra Mystica](#terra-mystica)
- [Tetris Link](#tetris-link)
- [The Resistance: Avalon](#the-resistance-avalon)
- [Ticket to Ride](#ticket-to-ride)
- [Ultimate Tic-Tac-Toe](#ultimate-tic-tac-toe)
- [UNO](#uno)
@ -111,6 +112,10 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub](
- [Dominion Strategy Forum](http://forum.dominionstrategy.com/index.php) (forumPost)
- [Clustering Player Strategies from Variable-Length Game Logs in Dominion](http://arxiv.org/abs/1811.11273) (journalArticle)
# Game Design
- [MDA: A Formal Approach to Game Design and Game Research](https://aaai.org/Library/Workshops/2004/ws04-04-001.php) (conferencePaper)
- [Exploring anonymity in cooperative board games](http://www.digra.org/digital-library/publications/exploring-anonymity-in-cooperative-board-games/) (conferencePaper)
# Hanabi
- [How to Make the Perfect Fireworks Display: Two Strategies forHanabi](https://doi.org/10.4169%2Fmath.mag.88.5.323) (journalArticle)
- [Evaluating and modelling Hanabi-playing agents](https://doi.org/10.1109%2Fcec.2017.7969465) (conferencePaper)
@ -137,6 +142,7 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub](
- [Playing mini-Hanabi card game with Q-learning](http://id.nii.ac.jp/1001/00205046/) (conferencePaper)
- [Hanabi Open Agent Dataset](https://github.com/aronsar/hoad) (computerProgram)
- [Hanabi Open Agent Dataset](https://dl.acm.org/doi/10.5555/3463952.3464188) (conferencePaper)
- [Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi](http://arxiv.org/abs/2107.07630) (journalArticle)
# Hive
- [On the complexity of Hive](https://dspace.library.uu.nl/handle/1874/396955) (thesis)
@ -179,7 +185,7 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub](
- [Trainyard is NP-Hard](http://arxiv.org/abs/1603.00928v1) (journalArticle)
- [Threes!, Fives, 1024!, and 2048 are Hard](http://arxiv.org/abs/1505.04274v1) (journalArticle)
# Modern Art
# Modern Art: The card game
- [A constraint programming based solver for Modern Art](https://github.com/captn3m0/modernart) (computerProgram)
# Monopoly
@ -219,6 +225,9 @@ 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)
# Resistance: Avalon
- [Finding Friend and Foe in Multi-Agent Games](http://arxiv.org/abs/1906.02330) (journalArticle)
# RISK
- [Mini-Risk: Strategies for a Simplified Board Game](https://doi.org/10.1057%2Fjors.1990.2) (journalArticle)
- [Learning the risk board game with classifier systems](https://doi.org/10.1145%2F508791.508904) (conferencePaper)
@ -270,9 +279,6 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub](
# Tetris Link
- [A New Challenge: Approaching Tetris Link with AI](http://arxiv.org/abs/2004.00377) (journalArticle)
# The Resistance: Avalon
- [Finding Friend and Foe in Multi-Agent Games](http://arxiv.org/abs/1906.02330) (journalArticle)
# Ticket to Ride
- [AI-based playtesting of contemporary board games](http://dl.acm.org/citation.cfm?doid=3102071.3102105) (conferencePaper)
- [Materials for Ticket to Ride Seattle and a framework for making more game boards](https://github.com/dovinmu/ttr_generator) (computerProgram)
@ -290,4 +296,12 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub](
# Yahtzee
- [Nearly Optimal Computer Play in Multi-player Yahtzee](https://doi.org/10.1007%2F978-3-642-17928-0_23) (bookSection)
- [Computer Strategies for Solitaire Yahtzee](https://doi.org/10.1109%2Fcig.2007.368089) (conferencePaper)
- [Modeling expert problem solving in a game of chance: a Yahtzeec case study](https://doi.org/10.1111%2F1468-0394.00160) (journalArticle)
- [Modeling expert problem solving in a game of chance: a Yahtzeec case study](https://doi.org/10.1111%2F1468-0394.00160) (journalArticle)
- [Probabilites In Yahtzee](https://pubs.nctm.org/view/journals/mt/75/9/article-p751.xml) (journalArticle)
- [Optimal Solitaire Yahtzee Strategies](http://www.yahtzee.org.uk/optimal_yahtzee_TV.pdf) (presentation)
- [Yahtzee: a Large Stochastic Environment for RL Benchmarks](http://researchers.lille.inria.fr/~lazaric/Webpage/PublicationsByTopic_files/bonarini2005yahtzee.pdf) (journalArticle)
- [Optimal Yahtzee performance in multi-player games](https://www.csc.kth.se/utbildning/kth/kurser/DD143X/dkand13/Group4Per/report/12-serra-widell-nigata.pdf) (thesis)
- [How to Maximize Your Score in Solitaire Yahtzee](http://www-set.win.tue.nl/~wstomv/misc/yahtzee/yahtzee-report-unfinished.pdf) (manuscript)
- [Using Deep Q-Learning to Compare Strategy Ladders of Yahtzee](https://raw.githubusercontent.com/philvasseur/Yahtzee-DQN-Thesis/dcf2bfe15c3b8c0ff3256f02dd3c0aabdbcbc9bb/webpage/final_report.pdf) (thesis)
- [Defensive Yahtzee](http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-168668) (report)
- [An Optimal Strategy for Yahtzee](http://www.cs.loyola.edu/~jglenn/research/optimal_yahtzee.pdf) (report)

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boardgame-research.rdf
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<dcterms:abstract>Teachers of units in probability are often interested in providing examples of probabilistic situations in a nonclassroom setting. Games are a rich source of such probabilities. Many people enjoy playing a commercial game called Yahtzee. A Yahtzee player receives points for achieving various specified numerical combinations of five dice during the three rolls that constitute a turn.</dcterms:abstract>
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<dc:title>Optimal Yahtzee performance in multi-player games</dc:title>
<dcterms:abstract>Yahtzee is a game with a moderately large search space, dependent on the factor of luck. This makes it not quite trivial to implement an optimal strategy for it. Using the optimal strategy for single-player
use, comparisons against other algorithms are made and the results are analyzed for hints on what it could take to make an algorithm that could beat the single-player optimal strategy.</dcterms:abstract>
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<dc:title>How to Maximize Your Score in Solitaire Yahtzee</dc:title>
<dcterms:abstract>Yahtzee is a well-known game played with five dice. Players take turns at assembling and scoring dice patterns. The player with the highest score wins. Solitaire Yahtzee is a single-player version of Yahtzee aimed at maximizing one’s score. A strategy for playing Yahtzee determines which choice to make in each situation of the game. We show that the maximum expected score over all Solitaire Yahtzee strategies is 254.5896. . . .</dcterms:abstract>
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<dc:title>Using Deep Q-Learning to Compare Strategy Ladders of Yahtzee</dc:title>
<dcterms:abstract>“Bots” playing games is not a new concept,
likely going back to the first video games. However,
there has been a new wave recently using machine
learning to learn to play games at a near optimal
level - essentially using neural networks to “solve”
games. Depending on the game, this can be relatively
straight forward using supervised learning. However,
this requires having data for optimal play, which is
often not possible due to the sheer complexity of many
games. For example, solitaire Yahtzee has this data
available, but two player Yahtzee does not due to the
massive state space. A recent trend in response to this
started with Google Deep Mind in 2013, who used Deep
Reinforcement Learning to play various Atari games
[4].
This project will apply Deep Reinforcement Learning
(specifically Deep Q-Learning) and measure how an
agent learns to play Yahtzee in the form of a strategy
ladder. A strategy ladder is a way of looking at how
the performance of an AI varies with the computational
resources it uses. Different sets of rules changes how the
the AI learns which varies the strategy ladder itself. This
project will vary the upper bonus threshold and then
attempt to measure how “good” the various strategy
ladders are - in essence attempting to find the set of
rules which creates the “best” version of Yahtzee. We
assume/expect that there is some correlation between
strategy ladders for AI and strategy ladders for human,
meaning that a game with a “good” strategy ladder for
an AI indicates that game is interesting and challenging
for humans.</dcterms:abstract>
<dc:date>December 12, 2019</dc:date>
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<foaf:name>KTH Royal Institute Of Technology Computer Science And Communication</foaf:name>
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<dc:title>Defensive Yahtzee</dc:title>
<dcterms:abstract>In this project an algorithm has been created that plays Yahtzee using rule
based heuristics. The focus is getting a high lowest score and a high 10th
percentile. All rules of Yahtzee and the probabilities for each combination
have been studied and based on this each turn is optimized to get a
guaranteed decent high score. The algorithm got a lowest score of 79 and a
10th percentile of 152 when executed 100 000 times.</dcterms:abstract>
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<dc:title>An Optimal Strategy for Yahtzee</dc:title>
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