1,143 research outputs found
How I won the "Chess Ratings - Elo vs the Rest of the World" Competition
This article discusses in detail the rating system that won the kaggle
competition "Chess Ratings: Elo vs the rest of the world". The competition
provided a historical dataset of outcomes for chess games, and aimed to
discover whether novel approaches can predict the outcomes of future games,
more accurately than the well-known Elo rating system. The winning rating
system, called Elo++ in the rest of the article, builds upon the Elo rating
system. Like Elo, Elo++ uses a single rating per player and predicts the
outcome of a game, by using a logistic curve over the difference in ratings of
the players. The major component of Elo++ is a regularization technique that
avoids overfitting these ratings. The dataset of chess games and outcomes is
relatively small and one has to be careful not to draw "too many conclusions"
out of the limited data. Many approaches tested in the competition showed signs
of such an overfitting. The leader-board was dominated by attempts that did a
very good job on a small test dataset, but couldn't generalize well on the
private hold-out dataset. The Elo++ regularization takes into account the
number of games per player, the recency of these games and the ratings of the
opponents. Finally, Elo++ employs a stochastic gradient descent scheme for
training the ratings, and uses only two global parameters (white's advantage
and regularization constant) that are optimized using cross-validation
Phoenix-Chess strategy or revisiting the algorithm for playing in Chess with incomplete information
We present here the new insight or revisiting the algorithm for playing in
Chess with incomplete information (which can be recognized by its newly
short-name as Phoenix-Chess strategy). The only difference with respect to the
classical variant of Chess-game is that each rook after its having been
captured by enemy chess piece in the proccess of gaming is not to be eliminated
from the current game, but this rook is assumed being under virtual repairing
during next N-steps (the required number of N is discussed in the current
research). Then afterwards, such rook will be introduced in game again during
maximal N-steps if only the chessboard square (on which it was captured
previously) has not been occupied at previous step. In this case, Phoenix-Chess
can be classified as game without predictable horizon of planning, so this kind
of game should be considered as Chess-like games with incomplete information.Comment: 14 pages, 5 Figures; Key words: Chess-like games, Phoenix-Chess
strategy, incomplete information. arXiv admin note: text overlap with
arXiv:2009.04374 by other author
Recent Advances in General Game Playing
The goal of General Game Playing (GGP) has been to develop computer programs that can perform well across various game types. It is natural for human game players to transfer knowledge from games they already know how to play to other similar games. GGP research attempts to design systems that work well across different game types, including unknown new games. In this review, we present a survey of recent advances (2011 to 2014) in GGP for both traditional games and video games. It is notable that research on GGP has been expanding into modern video games. Monte-Carlo Tree Search and its enhancements have been the most influential techniques in GGP for both research domains. Additionally, international competitions have become important events that promote and increase GGP research. Recently, a video GGP competition was launched. In this survey, we review recent progress in the most challenging research areas of Artificial Intelligence (AI) related to universal game playing
Poker as a Domain of Expertise
Poker is a game of skill and chance involving economic decision-making under uncertainty. It is also a complex but well-defined real-world environment with a clear rule-structure. As such, poker has strong potential as a model system for studying high-stakes, high-risk expert performance. Poker has been increasingly used as a tool to study decision-making and learning, as well as emotion self-regulation. In this review, we discuss how these studies have begun to inform us about the interaction between emotions and technical skill, and how expertise develops and depends on these two factors. Expertise in poker critically requires both mastery of the technical aspects of the game, and proficiency in emotion regulation; poker thus offers a good environment for studying these skills in controlled experimental settings of high external validity.We conclude by suggesting ideas for future research on expertise, with new insights provided by poker.Peer reviewe
PokerKit: A Comprehensive Python Library for Fine-Grained Multi-Variant Poker Game Simulations
PokerKit is an open-source Python library designed to overcome the
restrictions of existing poker game simulation and hand evaluation tools, which
typically support only a handful of poker variants and lack flexibility in game
state control. In contrast, PokerKit significantly expands this scope by
supporting an extensive array of poker variants and it provides a flexible
architecture for users to define their custom games. This paper details the
design and implementation of PokerKit, including its intuitive programmatic
API, multi-variant game support, and a unified hand evaluation suite across
different hand types. The flexibility of PokerKit allows for applications in
diverse areas, such as poker AI development, tool creation, and online poker
casino implementation. PokerKit's reliability has been established through
static type checking, extensive doctests, and unit tests, achieving 97\% code
coverage. The introduction of PokerKit represents a significant contribution to
the field of computer poker, fostering future research and advanced AI
development for a wide variety of poker games.Comment: 6 pages, 1 figure, submission to IEEE Transactions on Game
Tailoring a psychophysiologically driven rating system
Humans have always been interested in ways to measure and compare their performances to establish who is best at a particular activity. The first Olympic Games, for instance, were carried out in 776 BC, and it was a defining moment in history where ranking based competitive activities managed to reach the general populous. Every competition must face the issue of how to evaluate and rank competitors, and often rules are required to account for many different aspects such as variations in conditions, the ability to cheat, and, of course, the value of entertainment. Nowadays, measurements are performed out through various rating systems, which considers the outcomes of the activity to rate the participants. However, they do not seem to address the psychological aspects of an individual in a competition.
This dissertation employs several psychophysiological assessment instruments intending to facilitate the acquisition of skill level rating in competitive gaming. To do so, an exergame that uses non-conventional inputs, such as body tracking to prevent input biases, was developed. The sample size of this study is ten, and the participants were put on a round-robin tournament to provide equal intervals between games for each player.
After analyzing the outcome of the competition, it revealed some critical insights on the psychophysiological instruments; Especially the significance of Flow in terms of the prolificacy of a player. Although the findings did not provide an alternative for the traditional rating systems, it shows the importance of considering other aspects of the competition, such as psychophysiological metrics to fine-tune the rating. These potentially reveal more in-depth insight into the competition in comparison to just the binary outcome
A Match in Time Saves Nine: Deterministic Online Matching With Delays
We consider the problem of online Min-cost Perfect Matching with Delays
(MPMD) introduced by Emek et al. (STOC 2016). In this problem, an even number
of requests appear in a metric space at different times and the goal of an
online algorithm is to match them in pairs. In contrast to traditional online
matching problems, in MPMD all requests appear online and an algorithm can
match any pair of requests, but such decision may be delayed (e.g., to find a
better match). The cost is the sum of matching distances and the introduced
delays.
We present the first deterministic online algorithm for this problem. Its
competitive ratio is , where is the
number of requests. This is polynomial in the number of metric space points if
all requests are given at different points. In particular, the bound does not
depend on other parameters of the metric, such as its aspect ratio. Unlike
previous (randomized) solutions for the MPMD problem, our algorithm does not
need to know the metric space in advance
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