2014 IEEE Conference on Computational Intelligence and Games
The effectiveness of persuasion in The Settlers of Catan
2014 IEEE Conference on Computational Intelligence and Games (CIG)
International Journal of Gaming and Computer-Mediated Simulations
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
Deep Reinforcement Learning in Strategic Board Game Environments
Journal of the Operational Research Society
Mini-Risk: Strategies for a Simplified Board Game
Proceedings of the 2002 ACM symposium on Applied computing - SAC \textquotesingle02
Learning the risk board game with classifier systems
Markov Chains for the RISK Board Game Revisited
Planning an Endgame Move Set for the Game RISK: A Comparison of Search Algorithms
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
2018 IEEE Conference on Computational Intelligence and Games (CIG)
Monte Carlo Methods for the Game Kingdomino
How to Make the Perfect Fireworks Display: Two Strategies forHanabi
2017 IEEE Congress on Evolutionary Computation (CEC)
Evaluating and modelling Hanabi-playing agents
The Hanabi challenge: A new frontier for AI research
Diverse Agents for Ad-Hoc Cooperation in Hanabi
Ensemble Determinization in Monte Carlo Tree Search for the Imperfect Information Card Game Magic: The Gathering
Optimal Card-Collecting Strategies for Magic: The Gathering
2009 IEEE Symposium on Computational Intelligence and Games
Monte Carlo search applied to card selection in Magic: The Gathering
Case-Based Reasoning Research and Development
SCOUT: A Case-Based Reasoning Agent for Playing Race for the Galaxy
Woolford and Watson - 2017 - SCOUT A Case-Based Reasoning Agent for Playing Ra.pdf
Communications in Computer and Information Science
Implementation of Artificial Intelligence with 3 Different Characters of AI Player on “Monopoly Deal” Computer Game
Nearly Optimal Computer Play in Multi-player Yahtzee
2007 IEEE Symposium on Computational Intelligence and Games
Computer Strategies for Solitaire Yahtzee
Modeling expert problem solving in a game of chance: a Yahtzeec case study
Systematic Selection of N-Tuple Networks for 2048
2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)
Systematic selection of N-tuple networks with consideration of interinfluence for game 2048
An investigation into 2048 AI strategies
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
Improving Policies via Search in Cooperative Partially Observable Games
Hanabi is NP-hard, Even for Cheaters who Look at Their Cards
Generating and Adapting to Diverse Ad-Hoc Cooperation Agents in Hanabi
Evaluating the Rainbow DQN Agent in Hanabi with Unseen Partners
Magic: the Gathering is as Hard as Arithmetic
Neural Networks Models for Analyzing Magic: the Gathering Cards
Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence
Using Tabu Search Algorithm for Map Generation in the Terra Mystica Tabletop Game
Threes!, Fives, 1024!, and 2048 are Hard
Analysis of the Game "2048" and its Generalization in Higher Dimensions
Multi-Stage Temporal Difference Learning for 2048-like Games
2048 is (PSPACE) Hard, but Sometimes Easy
Settlers of Catan bot trained using reinforcement learning
Proceedings of the Annual Meeting of the Cognitive Science Society
Trading in a multiplayer board game: Towards an analysis of non-cooperative dialogue
Guhe and Lascarides - 2012 - Trading in a multiplayer board game Towards an an.pdf
POMCP with Human Preferencesin Settlers of Catan
POMCP with Human Preferencesin Settlers of Catan.pdf
Monte Carlo Tree Search in a Modern Board Game Framework
Reinforcement Learning of Strategies for Settlers of Catan
Pfeiffer - 2004 - Reinforcement Learning of Strategies for Settlers .pdf
An Intelligent Artificial Player for the Game of Risk
An Intelligent Artificial Player for the Game of R.pdf
RISKy Business: An In-Depth Look at the Game RISK
RISKy Business An In-Depth Look at the Game RISK.pdf
RISK Board Game ‐ Battle Outcome Analysis
RISK Board Game ‐ Battle Outcome Analysis.pdf
A multi-agent system for playing the board game risk
State Representation and Polyomino Placement for the Game Patchwork
Computer Science - Artificial Intelligence
Modern board games are a rich source of entertainment for many people, but also contain interesting and challenging structures for game playing research and implementing game playing agents. This paper studies the game Patchwork, a two player strategy game using polyomino tile drafting and placement. The core polyomino placement mechanic is implemented in a constraint model using regular constraints, extending and improving the model in (Lagerkvist, Pesant, 2008) with: explicit rotation handling; optional placements; and new constraints for resource usage. Crucial for implementing good game playing agents is to have great heuristics for guiding the search when faced with large branching factors. This paper divides placing tiles into two parts: a policy used for placing parts and an evaluation used to select among different placements. Policies are designed based on classical packing literature as well as common standard constraint programming heuristics. For evaluation, global propagation guided regret is introduced, choosing placements based on not ruling out later placements. Extensive evaluations are performed, showing the importance of using a good evaluation and that the proposed global propagation guided regret is a very effective guide.
Nmbr9 as a Constraint Programming Challenge
Modern board games are a rich source of interesting and new challenges for combinatorial problems. The game Nmbr9 is a solitaire style puzzle game using polyominoes. The rules of the game are simple to explain, but modelling the game effectively using constraint programming is hard. This abstract presents the game, contributes new generalized variants of the game suitable for benchmarking and testing, and describes a model for the presented variants. The question of the top possible score in the standard game is an open challenge.
Goodman - 2019 - Re-determinizing MCTS in Hanabi.pdf
Evolving Agents for the Hanabi 2018 CIG Competition
Aspects of the Cooperative Card Game Hanabi
Series Title: Communications in Computer and Information Science DOI: 10.1007/978-3-319-67468-1_7
Series Title: Lecture Notes in Computer Science DOI: 10.1007/978-3-319-71649-7_5
2017 IEEE Conference on Computational Intelligence and Games (CIG)
Solving Hanabi: Estimating Hands by Opponent's Actions in Cooperative Game with Incomplete Information
A unique behavior of humans is modifying one’s unobservable behavior based on the reaction of others for cooperation. We used a card game called Hanabi as an evaluation task of imitating human reflective intelligence with artificial intelligence. Hanabi is a cooperative card game with incomplete information. A player cooperates with an opponent in building several card sets constructed with the same color and ordered numbers. However, like a blind man's bluff, each player sees the cards of all other players except his/her own. Also, communication between players is restricted to information about the same numbers and colors, and the player is required to read his/his opponent's intention with the opponent's hand, estimate his/her cards with incomplete information, and play one of them for building a set. We compared human play with several simulated strategies. The results indicate that the strategy with feedbacks from simulated opponent's viewpoints achieves more score than other strategies.
Osawa - 2015 - Solving Hanabi Estimating Hands by Opponent's Act.pdf
A Browser-based Interface for the Exploration and Evaluation of Hanabi AIs
Eger and Martens - 2017 - A Browser-based Interface for the Exploration and .pdf
I see what you see: Integrating eye tracking into Hanabi playing agents
Humans’ eye movements convey a lot of information about their intentions, often unconsciously. Intelligent agents that cooperate with humans in various domains can beneﬁt from interpreting this information. This paper contains a preliminary look at how eye tracking could be useful for agents that play the cooperative card game Hanabi with human players. We outline several situations in which an AI agent can utilize gaze information, and present an outlook on how we plan to integrate this with reimplementations of contemporary Hanabi agents.
Gottwald et al. - I see what you see Integrating eye tracking into .pdf
State of the art Hanabi bots + simulation framework in rust
A strategy simulator for the well-known cooperative card game Hanabi
A framework for writing bots that play Hanabi
Jidoukan Jenga: Teaching English through remixing games and game rules
Let students play simple games in their L1. It’s ok! Then: You, the teacher, can help them critique the game in their L2. You, the teacher, can help them change the game in their L2. You, the teacher, can help them develop themselves. #dropthestick #dropthecarrot #bringmeaning
Teaching English through remixing games and game rules
📍 What is this? This is a recollection of a short lesson with some children. I used Jenga and a dictionary. 📍 Why did you make it? I want to show language teachers that simple games, and playing simple games in students’ first language can be a great foundation for helping students learn new vocabulary, think critically, and exercise creativity. 📍 Why is it radical? I taught using a simple board game (at a time when video games are over-focused on in research). I show what the learning looks like (I include a photo). The teaching and learning didn’t occur in a laboratory setting, but in the wild (in a community center). I focused on the learning around games. 📍 Who is it for? Language teachers can easily implement this lesson using Jenga or any other game. Language researchers can expand on the translating and remixing potential around games.
deHaan - 2020 - Jidoukan Jenga Teaching English through remixing .pdf
Meeple Centred Design: A Heuristic Toolkit for Evaluating the Accessibility of Tabletop Games
Eighteen Months of Meeple Like Us: An Exploration into the State of Board Game Accessibility
It is shown that for an arbitrary position of a Hive game where both players have the same set of N pieces it is PSPACE-hard to determine whether one of the players has a winning strategy. The proof is done by reducing the known PSPACE-complete set of true quantified boolean formulas to a game concerning these formulas, then to the game generalised geography, then to a version of that game with the restriction of having only nodes with maximum degree 3, and finally to generalised Hive. This thesis includes a short introduction to the subject of computational complexity.
Andel - 2020 - On the complexity of Hive.pdf
Creative Captioning: An AI Grand Challenge Based on the Dixit Board Game
We propose a new class of "grand challenge" AI problems that we call creative captioning---generating clever, interesting, or abstract captions for images, as well as understanding such captions. Creative captioning draws on core AI research areas of vision, natural language processing, narrative reasoning, and social reasoning, and across all these areas, it requires sophisticated uses of common sense and cultural knowledge. In this paper, we analyze several specific research problems that fall under creative captioning, using the popular board game Dixit as both inspiration and proposed testing ground. We expect that Dixit could serve as an engaging and motivating benchmark for creative captioning across numerous AI research communities for the coming 1-2 decades.
Proceedings of the International Conference on the Foundations of Digital Games - FDG '17
AI-based playtesting of contemporary board games
Materials for Ticket to Ride Seattle and a framework for making more game boards
The Difficulty of Learning Ticket to Ride
Ticket to Ride is a very popular, award-winning board-game where you try toscore the most points while building a railway spanning cities in America. For acomputer to learn to play this game is very difficult due to the vast state-actionspace. This project will explain why featurizing your state, and implementingcurriculum learning can help agents learn as state-action spaces grow too largefor traditional learning methods to be effective.
Proceedings of the 13th International Conference on the Foundations of Digital Games
Evolving maps and decks for ticket to ride
FDG '18: Foundations of Digital Games 2018
Playing Catan with Cross-dimensional Neural Network
Catan is a strategic board game having interesting properties, including multi-player, imperfect information, stochastic, complex state space structure (hexagonal board where each vertex, edge and face has its own features, cards for each player, etc), and a large action space (including negotiation). Therefore, it is challenging to build AI agents by Reinforcement Learning (RL for short), without domain knowledge nor heuristics. In this paper, we introduce cross-dimensional neural networks to handle a mixture of information sources and a wide variety of outputs, and empirically demonstrate that the network dramatically improves RL in Catan. We also show that, for the first time, a RL agent can outperform jsettler, the best heuristic agent available.
11th Hellenic Conference on Artificial Intelligence
Monte Carlo Tree Search for the Game of Diplomacy
SETN 2020: 11th Hellenic Conference on Artificial Intelligence
Operationalizing Intentionality to Play Hanabi with Human Players
Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
Behavioral Evaluation of Hanabi Rainbow DQN Agents and Rule-Based Agents
Hanabi is a multiplayer cooperative card game, where only your partners know your cards. All players succeed or fail together. This makes the game an excellent testbed for studying collaboration. Recently, it has been shown that deep neural networks can be trained through self-play to play the game very well. However, such agents generally do not play well with others. In this paper, we investigate the consequences of training Rainbow DQN agents with human-inspired rule-based agents. We analyze with which agents Rainbow agents learn to play well, and how well playing skill transfers to agents they were not trained with. We also analyze patterns of communication between agents to elucidate how collaboration happens. A key finding is that while most agents only learn to play well with partners seen during training, one particular agent leads the Rainbow algorithm towards a much more general policy. The metrics and hypotheses advanced in this paper can be used for further study of collaborative agents.
Playing mini-Hanabi card game with Q-learning
Competing in a Complex Hidden Role Game with Information Set Monte Carlo Tree Search
Advances in intelligent game playing agents have led to successes in perfect information games like Go and imperfect information games like Poker. The Information Set Monte Carlo Tree Search (ISMCTS) family of algorithms outperforms previous algorithms using Monte Carlo methods in imperfect information games. In this paper, Single Observer Information Set Monte Carlo Tree Search (SO-ISMCTS) is applied to Secret Hitler, a popular social deduction board game that combines traditional hidden role mechanics with the randomness of a card deck. This combination leads to a more complex information model than the hidden role and card deck mechanics alone. It is shown in 10108 simulated games that SO-ISMCTS plays as well as simpler rule based agents, and demonstrates the potential of ISMCTS algorithms in complicated information set domains.
Playing Carcassonne with Monte Carlo Tree Search
Monte Carlo Tree Search (MCTS) is a relatively new sampling method with multiple variants in the literature. They can be applied to a wide variety of challenging domains including board games, video games, and energy-based problems to mention a few. In this work, we explore the use of the vanilla MCTS and the MCTS with Rapid Action Value Estimation (MCTS-RAVE) in the game of Carcassonne, a stochastic game with a deceptive scoring system where limited research has been conducted. We compare the strengths of the MCTS-based methods with the Star2.5 algorithm, previously reported to yield competitive results in the game of Carcassonne when a domain-specific heuristic is used to evaluate the game states. We analyse the particularities of the strategies adopted by the algorithms when they share a common reward system. The MCTS-based methods consistently outperformed the Star2.5 algorithm given their ability to find and follow long-term strategies, with the vanilla MCTS exhibiting a more robust game-play than the MCTS-RAVE.
Computer Science - Computer Science and Game Theory
Quixo is a two-player game played on a 5$\times$5 grid where the players try to align five identical symbols. Specifics of the game require the usage of novel techniques. Using a combination of value iteration and backward induction, we propose the first complete analysis of the game. We describe memory-efficient data structures and algorithmic optimizations that make the game solvable within reasonable time and space constraints. Our main conclusion is that Quixo is a Draw game. The paper also contains the analysis of smaller boards and presents some interesting states extracted from our computations.
At Most 43 Moves, At Least 29: Optimal Strategies and Bounds for Ultimate Tic-Tac-Toe
Ultimate Tic-Tac-Toe is a variant of the well known tic-tac-toe (noughts and crosses) board game. Two players compete to win three aligned "fields", each of them being a tic-tac-toe game. Each move determines which field the next player must play in. We show that there exist a winning strategy for the first player, and therefore that there exist an optimal winning strategy taking at most 43 moves; that the second player can hold on at least 29 rounds; and identify any optimal strategy's first two moves.
A New Challenge: Approaching Tetris Link with AI
Decades of research have been invested in making computer programs for playing games such as Chess and Go. This paper focuses on a new game, Tetris Link, a board game that is still lacking any scientific analysis. Tetris Link has a large branching factor, hampering a traditional heuristic planning approach. We explore heuristic planning and two other approaches: Reinforcement Learning, Monte Carlo tree search. We document our approach and report on their relative performance in a tournament. Curiously, the heuristic approach is stronger than the planning/learning approaches. However, experienced human players easily win the majority of the matches against the heuristic planning AIs. We, therefore, surmise that Tetris Link is more difficult than expected. We offer our findings to the community as a challenge to improve upon.
Strategic Dialogue Management via Deep Reinforcement Learning
Artificially intelligent agents equipped with strategic skills that can negotiate during their interactions with other natural or artificial agents are still underdeveloped. This paper describes a successful application of Deep Reinforcement Learning (DRL) for training intelligent agents with strategic conversational skills, in a situated dialogue setting. Previous studies have modelled the behaviour of strategic agents using supervised learning and traditional reinforcement learning techniques, the latter using tabular representations or learning with linear function approximation. In this study, we apply DRL with a high-dimensional state space to the strategic board game of Settlers of Catan---where players can offer resources in exchange for others and they can also reply to offers made by other players. Our experimental results report that the DRL-based learnt policies significantly outperformed several baselines including random, rule-based, and supervised-based behaviours. The DRL-based policy has a 53% win rate versus 3 automated players (`bots'), whereas a supervised player trained on a dialogue corpus in this setting achieved only 27%, versus the same 3 bots. This result supports the claim that DRL is a promising framework for training dialogue systems, and strategic agents with negotiation abilities.
Applying Neural Networks and Genetic Programming to the Game Lost Cities
Ceramic: A research environment based on the multi-player strategic board game Azul
2013 International Conference on Field-Programmable Technology (FPT)
FPGA Blokus Duo Solver using a massively parallel architecture
The 17th CSI International Symposium on Computer Architecture & Digital Systems (CADS 2013)
2013 17th CSI International Symposium on Computer Architecture and Digital Systems (CADS)
Computer Science - Computation and Language
Dixit: Interactive Visual Storytelling via Term Manipulation
In this paper, we introduce Dixit, an interactive visual storytelling system that the user interacts with iteratively to compose a short story for a photo sequence. The user initiates the process by uploading a sequence of photos. Dixit first extracts text terms from each photo which describe the objects (e.g., boy, bike) or actions (e.g., sleep) in the photo, and then allows the user to add new terms or remove existing terms. Dixit then generates a short story based on these terms. Behind the scenes, Dixit uses an LSTM-based model trained on image caption data and FrameNet to distill terms from each image and utilizes a transformer decoder to compose a context-coherent story. Users change images or terms iteratively with Dixit to create the most ideal story. Dixit also allows users to manually edit and rate stories. The proposed procedure opens up possibilities for interpretable and controllable visual storytelling, allowing users to understand the story formation rationale and to intervene in the generation process.
Clustering Player Strategies from Variable-Length Game Logs in Dominion
We present a method for encoding game logs as numeric features in the card game Dominion. We then run the manifold learning algorithm t-SNE on these encodings to visualize the landscape of player strategies. By quantifying game states as the relative prevalence of cards in a player's deck, we create visualizations that capture qualitative differences in player strategies. Different ways of deviating from the starting game state appear as different rays in the visualization, giving it an intuitive explanation. This is a promising new direction for understanding player strategies across games that vary in length.
Human-Level Performance in No-Press Diplomacy via Equilibrium Search
Prior AI breakthroughs in complex games have focused on either the purely adversarial or purely cooperative settings. In contrast, Diplomacy is a game of shifting alliances that involves both cooperation and competition. For this reason, Diplomacy has proven to be a formidable research challenge. In this paper we describe an agent for the no-press variant of Diplomacy that combines supervised learning on human data with one-step lookahead search via regret minimization. Regret minimization techniques have been behind previous AI successes in adversarial games, most notably poker, but have not previously been shown to be successful in large-scale games involving cooperation. We show that our agent greatly exceeds the performance of past no-press Diplomacy bots, is unexploitable by expert humans, and ranks in the top 2% of human players when playing anonymous games on a popular Diplomacy website.
Maximum genus of the Jenga like configurations
We treat the boundary of the union of blocks in the Jenga game as a surface with a polyhedral structure and consider its genus. We generalize the game and determine the maximum genus of the generalized game.
Computer Science - Computer Vision and Pattern Recognition
Automatic Long-Term Deception Detection in Group Interaction Videos
Most work on automated deception detection (ADD) in video has two restrictions: (i) it focuses on a video of one person, and (ii) it focuses on a single act of deception in a one or two minute video. In this paper, we propose a new ADD framework which captures long term deception in a group setting. We study deception in the well-known Resistance game (like Mafia and Werewolf) which consists of 5-8 players of whom 2-3 are spies. Spies are deceptive throughout the game (typically 30-65 minutes) to keep their identity hidden. We develop an ensemble predictive model to identify spies in Resistance videos. We show that features from low-level and high-level video analysis are insufficient, but when combined with a new class of features that we call LiarRank, produce the best results. We achieve AUCs of over 0.70 in a fully automated setting. Our demo can be found at http://home.cs.dartmouth.edu/~mbolonkin/scan/demo/
Human-Side Strategies in the Werewolf Game Against the Stealth Werewolf Strategy
Series Title: Lecture Notes in Computer Science DOI: 10.1007/978-3-319-50935-8_9
Mafia can be described as an experiment in human psychology and mass hysteria, or as a game between informed minority and uninformed majority. Focus on a very restricted setting, Mossel et al. [to appear in Ann. Appl. Probab. Volume 18, Number 2] showed that in the mafia game without detectives, if the civilians and mafias both adopt the optimal randomized strategy, then the two groups have comparable probabilities of winning exactly when the total player size is R and the mafia size is of order Sqrt(R). They also proposed a conjecture which stated that this phenomenon should be valid in a more extensive framework. In this paper, we first indicate that the main theorem given by Mossel et al. [to appear in Ann. Appl. Probab. Volume 18, Number 2] can not guarantee their conclusion, i.e., the two groups have comparable winning probabilities when the mafia size is of order Sqrt(R). Then we give a theorem which validates the correctness of their conclusion. In the last, by proving the conjecture proposed by Mossel et al. [to appear in Ann. Appl. Probab. Volume 18, Number 2], we generalize the phenomenon to a more extensive framework, of which the mafia game without detectives is only a special case.
Neural Networks Models for Analyzing Magic
Series Title: Lecture Notes in Computer Science DOI: 10.1007/978-3-030-04179-3_20
The Complexity of Deciding Legality of a Single Step of Magic: The Gathering
Mathematical programming and Magic: The Gathering
Deck Construction Strategies for Magic: The Gathering
Deckbuilding in Magic: The Gathering Using a Genetic Algorithm
Magic: The Gathering Deck Performance Prediction
A constraint programming based solver for Modern Art
Learning Monopoly Gameplay: A Hybrid Model-Free Deep Reinforcement Learning and Imitation Learning Approach
Learning how to adapt and make real-time informed decisions in dynamic and complex environments is a challenging problem. To learn this task, Reinforcement Learning (RL) relies on an agent interacting with an environment and learning through trial and error to maximize the cumulative sum of rewards received by it. In multi-player Monopoly game, players have to make several decisions every turn which involves complex actions, such as making trades. This makes the decision-making harder and thus, introduces a highly complicated task for an RL agent to play and learn its winning strategies. In this paper, we introduce a Hybrid Model-Free Deep RL (DRL) approach that is capable of playing and learning winning strategies of the popular board game, Monopoly. To achieve this, our DRL agent (1) starts its learning process by imitating a rule-based agent (that resembles the human logic) to initialize its policy, (2) learns the successful actions, and improves its policy using DRL. Experimental results demonstrate an intelligent behavior of our proposed agent as it shows high win rates against different types of agent-players.
Proceedings 2001 IEEE International Symposium on Computational Intelligence in Robotics and Automation (Cat. No.01EX515)
Negotiation strategy of agents in the MONOPOLY game
2001 International Symposium on Computational Intelligence in Robotics and Automation
2012 IEEE Conference on Computational Intelligence and Games (CIG)
Generating interesting Monopoly boards from open data
Proceedings of the 2009 Winter Simulation Conference (WSC)
Estimating the probability that the game of Monopoly never ends
2009 Winter Simulation Conference (WSC 2009)
Learning to Play Monopoly with Monte Carlo Tree Search
TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON)
Learning to play Monopoly: A Reinforcement Learning approach
Finding Friend and Foe in Multi-Agent Games
Recent breakthroughs in AI for multi-agent games like Go, Poker, and Dota, have seen great strides in recent years. Yet none of these games address the real-life challenge of cooperation in the presence of unknown and uncertain teammates. This challenge is a key game mechanism in hidden role games. Here we develop the DeepRole algorithm, a multi-agent reinforcement learning agent that we test on The Resistance: Avalon, the most popular hidden role game. DeepRole combines counterfactual regret minimization (CFR) with deep value networks trained through self-play. Our algorithm integrates deductive reasoning into vector-form CFR to reason about joint beliefs and deduce partially observable actions. We augment deep value networks with constraints that yield interpretable representations of win probabilities. These innovations enable DeepRole to scale to the full Avalon game. Empirical game-theoretic methods show that DeepRole outperforms other hand-crafted and learned agents in five-player Avalon. DeepRole played with and against human players on the web in hybrid human-agent teams. We find that DeepRole outperforms human players as both a cooperator and a competitor.
A Mathematical Analysis of the Game of Santorini
Temporal difference learning of N-tuple networks for the game 2048
Computer Science - Computational Complexity
On the Complexity of Slide-and-Merge Games
We study the complexity of a particular class of board games, which we call `slide and merge' games. Namely, we consider 2048 and Threes, which are among the most popular games of their type. In both games, the player is required to slide all rows or columns of the board in one direction to create a high value tile by merging pairs of equal tiles into one with the sum of their values. This combines features from both block pushing and tile matching puzzles, like Push and Bejeweled, respectively. We define a number of natural decision problems on a suitable generalization of these games and prove NP-hardness for 2048 by reducing from 3SAT. Finally, we discuss the adaptation of our reduction to Threes and conjecture a similar result.
000 Computer science, knowledge, general works
Artwork Size: 14 pages Medium: application/pdf Publisher: Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH, Wadern/Saarbruecken, Germany
MDA: A Formal Approach to Game Design and Game Research
Exploring anonymity in cooperative board games
This study was done as a part of a larger research project where the interest was on exploring if and how gameplay design could give informative principles to the design of educational activities. The researchers conducted a series of studies trying to map game mechanics that had the special quality of being inclusive, i.e., playable by a diverse group of players. This specific study focused on designing a cooperative board game with the goal of implementing anonymity as a game mechanic. Inspired by the gameplay design patterns methodology (Björk & Holopainen 2005a; 2005b; Holopainen & Björk 2008), mechanics from existing cooperative board games were extracted and analyzed in order to inform the design process. The results from prototyping and play testing indicated that it is possible to implement anonymous actions in cooperative board games and that this mechanic made rather unique forms of gameplay possible. These design patterns can be further developed in order to address inclusive educational practices.
Computer Science - Human-Computer Interaction
Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi
Deep reinforcement learning has generated superhuman AI in competitive games such as Go and StarCraft. Can similar learning techniques create a superior AI teammate for human-machine collaborative games? Will humans prefer AI teammates that improve objective team performance or those that improve subjective metrics of trust? In this study, we perform a single-blind evaluation of teams of humans and AI agents in the cooperative card game Hanabi, with both rule-based and learning-based agents. In addition to the game score, used as an objective metric of the human-AI team performance, we also quantify subjective measures of the human's perceived performance, teamwork, interpretability, trust, and overall preference of AI teammate. We find that humans have a clear preference toward a rule-based AI teammate (SmartBot) over a state-of-the-art learning-based AI teammate (Other-Play) across nearly all subjective metrics, and generally view the learning-based agent negatively, despite no statistical difference in the game score. This result has implications for future AI design and reinforcement learning benchmarking, highlighting the need to incorporate subjective metrics of human-AI teaming rather than a singular focus on objective task performance.
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.
Yahtzee: a Large Stochastic Environment for RL Benchmarks
Yahtzee is a game that is regularly played by more than 100 million people in the world. We propose a simplified version of Yahtzee as a benchmark for RL algorithms. We have already used it for this purpose, and an implementation is available.
KTH, School of Computer Science and Communication (CSC)
Optimal Yahtzee performance in multi-player games
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.
Independent thesis Basic level (degree of Bachelor)
How to Maximize Your Score in Solitaire Yahtzee
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. . . .
Yale University, Department of Computer Science
Using Deep Q-Learning to Compare Strategy Ladders of Yahtzee
“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.
KTH Royal Institute Of Technology Computer Science And Communication
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.
Applications of Graph Theory and Probability in the Board Game Ticket to Ride