Nemo
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README.md
boardgame-research 
This is a list of boardgame research. They are primarily related to "solving/playing/learning" 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. If you are interested in well-researched games like Chess, Go, Hex, take a look at the Chess programming wiki instead.
Table of Contents generated with DocToc
- Settlers of Catan
- Diplomacy
- Risk
- Kingdomino
- Patchwork
- Nmbr9
- Hanabi
- Monopoly
- Magic: the Gathering
- Terra Mystica
- Dominion
- Mafia
- The Resistance: Avalon
- Ticket to Ride
- Yahtzee
- Lost Cities
- Uno
- Dominion
- Mobile Games
- Game Design
- Frameworks/Toolkits
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
Patchwork
Nmbr9
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
- Aspects of the Cooperative Card Game Hanabi
- How to Make the Perfect Fireworks Display: Two Strategies for Hanabi
- 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]] (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 (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
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
Dominion
Mafia
- A mathematical model of the Mafia game
- Automatic Long-Term Deception Detection in Group Interaction Videos
The Resistance: Avalon
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
Uno
Dominion
I couldn't find any published research on Dominion, but there is a simulator and the code behind the Dominion server running councilroom.com is available. There are best and worst openings, optimal card ratios, Card winning stats and lots of other interesting stuff.
Mobile Games
2048
- Making Change in 2048
- Analysis of the Game "2048" and its Generalization in Higher Dimensions
- Temporal difference learning of N-tuple networks for the game 2048
- Multi-Stage Temporal Difference Learning for 2048-like Games
- On the Complexity of Slide-and-Merge Games
- 2048 is (PSPACE) Hard, but Sometimes Eas
- Systematic Selection of N-Tuple Networks for 2048
- Systematic selection of N-tuple networks with consideration of interinfluence for game 2048
- 2048 Without New Tiles Is Still Hard
- An investigation into 2048 AI strategies