Mooooar links

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# Go
# boardgame-research
This is a list of boardgame related research papers, blogs. They are primarily related to "solving" games (by various different approaches), or
occasionaly about designing or meta-aspects of the game. This doesn't cover all aspects of each game (notably missing social-science stuff), but
should be of interest to anyone interested in boardgames and their optimal play. While there is a ton of easily accessible research on games like
Chess and Go, finding prior work on more contemporary games can be a bit hard. This list focuses on the latter.
# Settlers of Catan
- [The effectiveness of persuasion in The Settlers of Catan ](
- [Avoiding Revenge Using Optimal Opponent Ranking Strategy in the Board Game Catan ](
- [Game strategies for The Settlers of Catan](
- [Monte-Carlo Tree Search in Settlers of Catan](
- [Settlers of Catan bot trained using reinforcement learning (MATLAB).](
- [Trading in a multiplayer board game:Towards an analysis of non-cooperative dialogue](
- [POMCP with Human Preferencesin Settlers of Catan](
- [Reinforcement Learning of Strategies for Settlers of Catan](
- [Deep Reinforcement Learning in Strategic Board GameEnvironments]( [[pdf](]
- [Monte Carlo Tree Search in a Modern Board Game Framework](
# Diplomacy
- [Learning to Play No-Press Diplomacy with Best Response Policy Iteration ](
- [No Press Diplomacy: Modeling Multi-Agent Gameplay ](
- [Agent Madoff: A Heuristic-Based Negotiation Agent For The Diplomacy Strategy Game ](
# Risk
- [Mini-Risk: Strategies for a Simplified Board Game](
- [A Multi-Agent System for playing theboard game Risk](
- [Learning the risk board game with classifier systems](
- [Markov Chains and the RISK Board Game](
- [Markov Chains for the RISK Board Game Revisited](
- [RISK Board Game Battle Outcome Analysis](
- [Planning an endgame move set for the game RISK](
- [RISKy Business: An In-Depth Look at the Game RISK](
- [An Intelligent Artificial Player for the Game of Risk](
# Kingdomino
- [Monte Carlo Methods for the Game Kingdomino]( [[arXiv](]
- [NP-completeness of the game Kingdomino](
# Patchwork
- [State Representation and Polyomino Placement for the Game Patchwork](
# Nmbr9
- [Nmbr9 as a Constraint Programming Challenge](
# Hanabi
- [Re-determinizing MCTS in Hanabi](
- [Hanabi is NP-hard, Even for Cheaters who Look at Their Cards](
- [Evolving Agents for the Hanabi 2018 CIG Competition](
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- [Playing Hanabi Near-Optimally](
- [Evaluating and modelling Hanabi-playing agents](
- [An intentional AI for hanabi](
- [The Hanabi challenge: A new frontier for AI research]( [[arXiv](]
- [The Hanabi challenge: A new frontier for AI research]( [[arXiv](]] (DeepMind)
- [Solving Hanabi: Estimating Hands by Opponent's Actions in Cooperative Game with Incomplete Information](
- [A Browser-based Interface for the Exploration and Evaluation of Hanabi AIs](
- [I see what you see: Integrating eye tracking into Hanabi playing agents](
- [The 2018 Hanabi competition](
- [Diverse Agents for Ad-Hoc Cooperation in Hanabi]( [[arXiv](]
- [State of the art Hanabi bots + simulation framework in rust](
- [Improving Policies via Search in Cooperative Partially Observable Games](
- [Improving Policies via Search in Cooperative Partially Observable Games]( (FB) [[code](]
- [A strategy simulator for the well-known cooperative card game Hanabi](
- [A framework for writing bots that play Hanabi](
- [Evaluating the Rainbow DQN Agent in Hanabi with Unseen Partners](
- [Evaluating the Rainbow DQN Agent in Hanabi with Unseen Partners](
# Monopoly
- [Negotiation strategy of agents in the MONOPOLY game](
- [Generating interesting Monopoly boards from open data](
- [Estimating the probability that the game of Monopoly never ends](
- [Learning to play Monopoly:A Reinforcement Learning approach](
- [Monopoly as a Markov Process](
- [Learning to Play Monopoly withMonte Carlo Tree Search](
- [Monopoly Using Reinforcement Learning ](
- [A Markovian Exploration of Monopoly](
- [What's the best Monopoly strategy](
# Magic: the Gathering
- [Magic: the Gathering is as Hard as Arithmetic](
- [Magic: The Gathering is Turing Complete](
- [Neural Networks Models for Analyzing Magic: the Gathering Cards](
- [Ensemble Determinization in Monte Carlo Tree Search for the Imperfect Information Card Game Magic: The Gathering](
- [Deckbuilding in Magic: The Gathering Using a Genetic Algorithm](
# Terra Mystica
- [Using Tabu Search Algorithm for Map Generation in the Terra Mystica Tabletop Game](
# Dominion
- [Clustering Player Strategies from Variable-Length Game Logs in Dominion](
# Mafia
- [A mathematical model of the Mafia game](
- [Automatic Long-Term Deception Detection in Group Interaction Videos](
# The Resistance: Avalon
- [Finding Friend and Foe in Multi-Agent Games](
# Ticket to Ride
- [Evolving maps and decks for ticket to ride](
- [Materials for Ticket to Ride Seattle and a framework for making more game boards](
- [Applications of Graph Theory andProbability in the Board GameTicket toRide](
- [The Difficulty of Learning Ticket to Ride](
# Yahtzee
- [Optimal Solitaire Yahtzee Strategies](
- [Nearly Optimal Computer Play in Multi-player Yahtzee](
- [Computer Strategies for Solitaire Yahtzee](
- [An optimal strategy for Yahtzee](
- [Yahtzee: a Large Stochastic Environment for RL Benchmarks](
- [Modeling expert problem solving in a game of chance: a Yahtzee case study](
- [Probabilites In Yahtzee](
- [Optimal Yahtzee performance in multi-player games](
- [Defensive Yahtzee](
- [Using Deep Q-Learning to Compare Strategy Ladders of Yahtzee](
- [How to Maximize Your Score in Solitaire Yahtzee](
# Lost Cities
- [Applying Neural Networks and Genetic Programming to the Game Lost Cities](
# Design
- [MDA: A Formal Approach to Game Design and Game Research ](