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<dc:title>2017 IEEE Conference on Computational Intelligence and Games (CIG)</dc:title>
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<dc:title>Solving Hanabi: Estimating Hands by Opponent's Actions in Cooperative Game with Incomplete Information</dc:title>
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<dcterms:abstract>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.</dcterms:abstract>
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<dc:date>2015</dc:date>
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<dc:title>A Browser-based Interface for the Exploration and Evaluation of Hanabi AIs</dc:title>
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<dc:date>2017</dc:date>
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<dcterms:abstract>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.</dcterms:abstract>
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<dc:title>Jidoukan Jenga: Teaching English through remixing games and game rules</dc:title>
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<dcterms:abstract>Let students play simple games in their L1. It’s ok!
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Then:
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You, the teacher, can help them critique the game in their L2.
|
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You, the teacher, can help them change the game in their L2.
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You, the teacher, can help them develop themselves.
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#dropthestick #dropthecarrot #bringmeaning</dcterms:abstract>
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|
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📍 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.
|
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📍 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.</dc:description>
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<dcterms:abstract>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.</dcterms:abstract>
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<dc:date>May 2020</dc:date>
|
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<dcterms:abstract>&lt;p class=&quot;abstract&quot;&gt;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.&lt;/p&gt;</dcterms:abstract>
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|
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<dcterms:abstract>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.</dcterms:abstract>
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<dc:date>2008-04-01</dc:date>
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<dc:description>arXiv: 0804.0071</dc:description>
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<link:link rdf:resource="#item_406"/>
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<dc:title>Neural Networks Models for Analyzing Magic: The Gathering Cards</dc:title>
|
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<dc:date>2018</dc:date>
|
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<dc:description>Series Title: Lecture Notes in Computer Science
|
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DOI: 10.1007/978-3-030-04179-3_20</dc:description>
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<bib:pages>227-239</bib:pages>
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<dc:title>The Complexity of Deciding Legality of a Single Step of Magic: The Gathering</dc:title>
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|
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|
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<dcterms:abstract>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.</dcterms:abstract>
|
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<dc:date>2021-02-28</dc:date>
|
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|
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|
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<dc:title>Proceedings 2001 IEEE International Symposium on Computational Intelligence in Robotics and Automation (Cat. No.01EX515)</dc:title>
|
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<dc:identifier>DOI 10.1109/CIRA.2001.1013210</dc:identifier>
|
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|
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|
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|
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<link:link rdf:resource="#item_426"/>
|
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<dc:title>Negotiation strategy of agents in the MONOPOLY game</dc:title>
|
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<dc:date>2001</dc:date>
|
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<dcterms:abstract>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.</dcterms:abstract>
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<dc:date>2019-06-05</dc:date>
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<dcterms:abstract>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.</dcterms:abstract>
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|
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|
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<dc:title>Think Design Play</dc:title>
|
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|
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|
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|
||
<dcterms:abstract>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.</dcterms:abstract>
|
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|
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|
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|
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<dc:title>Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi</dc:title>
|
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<dcterms:abstract>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.</dcterms:abstract>
|
||
<dc:date>2021-07-19</dc:date>
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|
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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>Yahtzee: a Large Stochastic Environment for RL Benchmarks</dc:title>
|
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<dcterms:abstract>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
|
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used it for this purpose, and an implementation is available.</dcterms:abstract>
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|
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|
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|
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|
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|
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|
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<dc:title>Optimal Yahtzee performance in multi-player games</dc:title>
|
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<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:date>April 12, 2013</dc:date>
|
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|
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|
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<z:type>Independent thesis Basic level (degree of Bachelor)</z:type>
|
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|
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|
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|
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|
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|
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|
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<dc:title>How to Maximize Your Score in Solitaire Yahtzee</dc:title>
|
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<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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|
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|
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|
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|
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|
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|
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|
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|
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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>
|
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<dc:date>December 12, 2019</dc:date>
|
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|
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|
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|
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|
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|
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|
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|
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|
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</dc:publisher>
|
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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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|
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|
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|
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<bib:pages>22</bib:pages>
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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<link:link rdf:resource="#item_478"/>
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|
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|
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<dc:title>Strategic Dialogue Management via Deep Reinforcement Learning</dc:title>
|
||
<dcterms:abstract>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.</dcterms:abstract>
|
||
<dc:date>2015-11-25</dc:date>
|
||
<z:libraryCatalog>arXiv.org</z:libraryCatalog>
|
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