58,664 research outputs found
Statistical Feature Combination for the Evaluation of Game Positions
This article describes an application of three well-known statistical methods
in the field of game-tree search: using a large number of classified Othello
positions, feature weights for evaluation functions with a
game-phase-independent meaning are estimated by means of logistic regression,
Fisher's linear discriminant, and the quadratic discriminant function for
normally distributed features. Thereafter, the playing strengths are compared
by means of tournaments between the resulting versions of a world-class Othello
program. In this application, logistic regression - which is used here for the
first time in the context of game playing - leads to better results than the
other approaches.Comment: See http://www.jair.org/ for any accompanying file
Dynamic Move Chains -- a Forward Pruning Approach to Tree Search in Computer Chess
This paper proposes a new mechanism for pruning a search game-tree in
computer chess. The algorithm stores and then reuses chains or sequences of
moves, built up from previous searches. These move sequences have a built-in
forward-pruning mechanism that can radically reduce the search space. A typical
search process might retrieve a move from a Transposition Table, where the
decision of what move to retrieve would be based on the position itself. This
algorithm stores move sequences based on what previous sequences were better,
or caused cutoffs. This is therefore position independent and so it could also
be useful in games with imperfect information or uncertainty, where the whole
situation is not known at any one time. Over a small set of tests, the
algorithm was shown to clearly out-perform Transposition Tables, both in terms
of search reduction and game-play results.Comment: Publishe
Fantasy Football Prediction
The ubiquity of professional sports and specifically the NFL have lead to an
increase in popularity for Fantasy Football. Users have many tools at their
disposal: statistics, predictions, rankings of experts and even recommendations
of peers. There are issues with all of these, though. Especially since many
people pay money to play, the prediction tools should be enhanced as they
provide unbiased and easy-to-use assistance for users. This paper provides and
discusses approaches to predict Fantasy Football scores of Quarterbacks with
relatively limited data. In addition to that, it includes several suggestions
on how the data could be enhanced to achieve better results. The dataset
consists only of game data from the last six NFL seasons. I used two different
methods to predict the Fantasy Football scores of NFL players: Support Vector
Regression (SVR) and Neural Networks. The results of both are promising given
the limited data that was used.Comment: class project, 7 pages (1 sources, 1 appendix
Chess Endgames and Neural Networks
The existence of endgame databases challenges us to extract higher-grade information and knowledge from their basic data content. Chess players, for example, would like simple and usable endgame theories if such holy grail exists: endgame experts would like to provide such insights and be inspired by computers to do so. Here, we investigate the use of artificial neural networks (NNs) to mine these databases and we report on a first use of NNs on KPK. The results encourage us to suggest further work on chess applications of neural networks and other data-mining techniques
Artificial Intelligence as Structural Estimation: Economic Interpretations of Deep Blue, Bonanza, and AlphaGo
Artificial intelligence (AI) has achieved superhuman performance in a growing
number of tasks, but understanding and explaining AI remain challenging. This
paper clarifies the connections between machine-learning algorithms to develop
AIs and the econometrics of dynamic structural models through the case studies
of three famous game AIs. Chess-playing Deep Blue is a calibrated value
function, whereas shogi-playing Bonanza is an estimated value function via
Rust's (1987) nested fixed-point method. AlphaGo's "supervised-learning policy
network" is a deep neural network implementation of Hotz and Miller's (1993)
conditional choice probability estimation; its "reinforcement-learning value
network" is equivalent to Hotz, Miller, Sanders, and Smith's (1994) conditional
choice simulation method. Relaxing these AIs' implicit econometric assumptions
would improve their structural interpretability
A Comparism of the Performance of Supervised and Unsupervised Machine Learning Techniques in evolving Awale/Mancala/Ayo Game Player
Awale games have become widely recognized across the world, for their
innovative strategies and techniques which were used in evolving the agents
(player) and have produced interesting results under various conditions. This
paper will compare the results of the two major machine learning techniques by
reviewing their performance when using minimax, endgame database, a combination
of both techniques or other techniques, and will determine which are the best
techniques.Comment: 10 pages, 2 figure
A Computer Composes A Fabled Problem: Four Knights vs. Queen
We explain how the prototype automatic chess problem composer, Chesthetica,
successfully composed a rare and interesting chess problem using the new
Digital Synaptic Neural Substrate (DSNS) computational creativity approach.
This problem represents a greater challenge from a creative standpoint because
the checkmate is not always clear and the method of winning even less so.
Creating a decisive chess problem of this type without the aid of an omniscient
7-piece endgame tablebase (and one that also abides by several chess
composition conventions) would therefore be a challenge for most human players
and composers working on their own. The fact that a small computer with
relatively low processing power and memory was sufficient to compose such a
problem using the DSNS approach in just 10 days is therefore noteworthy. In
this report we document the event and result in some detail. It lends
additional credence to the DSNS as a viable new approach in the field of
computational creativity. In particular, in areas where human-like creativity
is required for targeted or specific problems with no clear path to the
solution.Comment: 12 pages, 5 figures and 2 appendice
A Survey on Content-Aware Video Analysis for Sports
Sports data analysis is becoming increasingly large-scale, diversified, and
shared, but difficulty persists in rapidly accessing the most crucial
information. Previous surveys have focused on the methodologies of sports video
analysis from the spatiotemporal viewpoint instead of a content-based
viewpoint, and few of these studies have considered semantics. This study
develops a deeper interpretation of content-aware sports video analysis by
examining the insight offered by research into the structure of content under
different scenarios. On the basis of this insight, we provide an overview of
the themes particularly relevant to the research on content-aware systems for
broadcast sports. Specifically, we focus on the video content analysis
techniques applied in sportscasts over the past decade from the perspectives of
fundamentals and general review, a content hierarchical model, and trends and
challenges. Content-aware analysis methods are discussed with respect to
object-, event-, and context-oriented groups. In each group, the gap between
sensation and content excitement must be bridged using proper strategies. In
this regard, a content-aware approach is required to determine user demands.
Finally, the paper summarizes the future trends and challenges for sports video
analysis. We believe that our findings can advance the field of research on
content-aware video analysis for broadcast sports.Comment: Accepted for publication in IEEE Transactions on Circuits and Systems
for Video Technology (TCSVT
Induction, of and by Probability
This paper examines some methods and ideas underlying the author's successful
probabilistic learning systems(PLS), which have proven uniquely effective and
efficient in generalization learning or induction. While the emerging
principles are generally applicable, this paper illustrates them in heuristic
search, which demands noise management and incremental learning. In our
approach, both task performance and learning are guided by probability.
Probabilities are incrementally normalized and revised, and their errors are
located and corrected.Comment: Appears in Proceedings of the First Conference on Uncertainty in
Artificial Intelligence (UAI1985
Proceedings of Mathsport international 2017 conference
Proceedings of MathSport International 2017 Conference, held in the Botanical Garden of the University of Padua, June 26-28, 2017.
MathSport International organizes biennial conferences dedicated to all topics where mathematics and sport meet.
Topics include: performance measures, optimization of sports performance, statistics and probability models, mathematical and physical models in sports, competitive strategies, statistics and probability match outcome models, optimal tournament design and scheduling, decision support systems, analysis of rules and adjudication, econometrics in sport, analysis of sporting technologies, financial valuation in sport, e-sports (gaming), betting and sports
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