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Move Prediction in Go with the Maximum Entropy Method

Nobuo Araki, Kazuhiro Yoshida, Yoshimasa Tsuruoka, Jun'ichi Tsujii
2007 2007 IEEE Symposium on Computational Intelligence and Games  
We address the problem of predicting moves in the board game of Go.  ...  We use the relative frequencies of local board patterns observed in game records to generate a ranked list of moves, and then apply the maximum entropy method (MEM) to the list to re-rank the moves.  ...  We used the maximum entropy method (MEM) to predict moves. There have been several previous studies on predicting moves in Go.  ... 
doi:10.1109/cig.2007.368097 dblp:conf/cig/ArakiYTT07 fatcat:fn3qqthmfjcoxct4mew7jeqmeu

Maximum Entropy Production Principle for Stock Returns [article]

Paweł Fiedor
2014 arXiv   pre-print
To find a way to use the structural complexity of the stock returns for the purpose of predictions we propose the Maximum Entropy Production Principle as applied to stock returns, and test it on the two  ...  We have also correlated the estimated predictability with the profitability of standard trading algorithms, and found that these do not use the structure inherent in the stock returns to any significant  ...  In Section 2 we present a method used to estimate entropy production in price formation processes, and the setup for testing the Maximum Entropy Production Principle.  ... 
arXiv:1408.3728v1 fatcat:oqq3m6rbunfqvedajbr7jza2c4

Comparison of Frequency Bands Using Spectral Entropy for Epileptic Seizure Prediction

Susana Blanco, Arturo Garay, Diego Coulombie
2013 ISRN Neurology  
The entropy rate of growth in the low-frequency band is practically zero, with a correlation around 0.2 and mostly positive slopes.  ...  The aim of this study is to compare the results of spectral entropies calculated in different frequency bands of the EEG signals to decide which band may be a better tool to predict an epileptic seizure  ...  The one with less dispersion means greater certainty in the measurement. Materials and Methods Materials.  ... 
doi:10.1155/2013/287327 pmid:23781347 pmcid:PMC3677650 fatcat:eri4tmh6mfgnbdlz6gcxkpprpy

An imperceptible & robust digital image watermarking scheme based on DWT, entropy and neural network

M Saiful Islam, Muhammad Ahsan Ullah, Jitu Prakash Dhar
2019 Karbala International Journal of Modern Science  
Then, the entropy of each frequency sub-band is calculated to find the maximum entropy sub-band in order to embed the highest entropy sub-band of the watermark image into the highest entropy sub-band of  ...  Experimental results demonstrate that this proposed method not only has the superior robustness and imperceptibility but also has improved the anti-attack capability in comparison with recent works in  ...  It can be observed that with the increase of entropy the PSNR value is rising significantly. Also, the NC value is growing sharply when we are moving towards the maximum entropy region.  ... 
doi:10.33640/2405-609x.1068 fatcat:fp77v3mtsnb3tjnbcqv363djdi

Fast Adaptive Selection of Best Views [chapter]

Pere-Pau Vázquez, Mateu Sbert
2003 Lecture Notes in Computer Science  
In this paper we present an adaptive method to compute best views.  ...  To select the most interesting viewpoint of an object, we use the so-called viewpoint entropy. The best view is the one which gives the most information of the object being inspected.  ...  , Research and the Information Society from Catalan Government.  ... 
doi:10.1007/3-540-44842-x_31 fatcat:ugw2d3b4obe4rkt5kgk2wqpwvu

Action principle and Jaynes' guess method [article]

Q. A. Wang
2004 arXiv   pre-print
A path information is defined in connection with the probability distribution of paths of nonequilibrium hamiltonian systems moving in phase space from an initial cell to different final cells.  ...  Hence the principle of maximum path information suggests maximum entropy and entropy change which, in other words, is just an application of the action principle of classical mechanics to the cases of  ...  ., "if a system is in a state with lower entropy it would contain more information than previously specified", or "higher entropy states are more probable" and "less predictable", etc [1, 14] .  ... 
arXiv:cond-mat/0407515v1 fatcat:53uooni4zbf3dn22iq45hn3pg4

The Entropy of Artificial Intelligence and a Case Study of AlphaZero from Shannon's Perspective [article]

Bo Zhang, Bin Chen, Jin-lin Peng
2018 arXiv   pre-print
The recently released AlphaZero algorithm achieves superhuman performance in the games of chess, shogi and Go, which raises two open questions.  ...  Finally, conclusions are provided along with theoretical and practical remarks.  ...  In chess or Go, both agents try to win the game so that each agent tries to predict the behavior of each other.  ... 
arXiv:1812.05794v2 fatcat:caue526ruzfmjheklvdhy5p3e4

Scale matters: The daily, weekly and monthly volatility and predictability of Bitcoin, Gold, and the S P 500 [article]

Nassim Dehouche
2021 arXiv   pre-print
We show that, historically, Bitcoin allies both high volatility (high Standard Deviation) and high predictability (low Approximate Entropy), relative to Gold and S&P 500.  ...  We find that the closing price of Bitcoin is consistent with a generalized Pareto distribution, when the closing prices of the two other assets (Gold and S&P 500) present thin-tailed distributions.  ...  Moving Approximate Entropy of closing prices Figure 13 : 13 a) 100-day moving Approximate Entropy of daily log-returns (b) 20-week moving Approximate Entropy of weekly log-returns (c) 3-month moving  ... 
arXiv:2103.00395v1 fatcat:hcrfqxcqrnbetmyysyiyb3iuwm

Sparse Trajectory Prediction Method Based on Entropy Estimation

Lei ZHANG, Leijun LIU, Wen LI
2016 IEICE transactions on information and systems  
Compared with the existing methods, our method needs a smaller trajectory space and provides much wider predicting range, faster predicting speed and better predicting accuracy.  ...  In addition, the trajectory scale is limited in a certain range. Finally, under the new trajectory space, Markov model and Bayesian Inference is applied to trajectory prediction with data sparsity.  ...  Acknowledgements This work was supported by the Fundamental Research Funds for the Central Universities (2014XT04).  ... 
doi:10.1587/transinf.2015cbp0001 fatcat:5oubuv3e4rdtvmyb33eb2bxuia

The Evolution of Carnot's Principle [chapter]

E. T. Jaynes
1988 Maximum-Entropy and Bayesian Methods in Science and Engineering  
Both the logic and the procedure of our present maximum entropy applications are easily recognized in the methods for predicting equilibrium conditions introduced by Gibbs in 1875.  ...  What is new in this eld is not the method, but the recognition of its generality.  ...  Then in Part 7 the new" maximum entropy methods will be seen as just one more step in the natural development of the subject.  ... 
doi:10.1007/978-94-009-3049-0_15 fatcat:ovh6jyvzgvgrhas4xloakojyja

Optimized Distributed Proactive Caching Based on Movement Probability of Vehicles in Content-Centric Vehicular Networks

Seungmin Oh, Sungjin Park, Yongje Shin, Euisin Lee
2022 Sensors  
Next, we expand the protocol with traffic optimization called MDPC_TO to minimize the amount of traffic for achieving proactive caching in CCVNs.  ...  For recovery of prediction failures, MDPC allows each RSU to request partial missing contents to relatively closer neighbor RSUs with a short delay.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s22093346 pmid:35591034 pmcid:PMC9101238 fatcat:szp6ovnafvbnphv6vbrrj27elu

Kernel Entropy Based Extended Kalman Filter for GPS Navigation Processing

Dah-Jing Jwo, Jui-Tao Lee
2021 Computers Materials & Continua  
The kernel minimum error entropy (MEE) and maximum correntropy criterion (MCC) based filtering for satellite navigation system is involved for dealing with non-Gaussian errors or heavy-tailed interference  ...  The standard EKF method is derived based on minimization of mean square error (MSE) and is optimal only under Gaussian assumption in case the system models are precisely established.  ...  Entropy From the definition of the information theoretic and kernel methods, entropy is a measure of the uncertainty associated with random variables.  ... 
doi:10.32604/cmc.2021.016894 fatcat:lkvlw2osc5horknujf6gh3dz54

Development Of Maximum Entropy Method For Prediction Of Droplet-Size Distribution In Primary Breakup Region Of Spray

E. Movahednejad, F. Ommi
2011 Zenodo  
This paper describes the development of a new model to be incorporated into maximum entropy principle (MEP) formalism for prediction of droplet size distribution in droplet formation region.  ...  The purpose of this paper is to describe the formulation of this new model and to incorporate it into the maximum entropy principle (MEP) by coupling sub-models together using source terms of momentum  ...  ACKNOWLEDGMENTS Some part of the work presented in this paper was performed while the lead author (E. M.) was working at a MAPNA Turbine Engineering & Manufacturing Co. (TUGA).  ... 
doi:10.5281/zenodo.1071434 fatcat:tgtpzltjx5a4hngooawv74ugay

Application of complex demodulation on bZIP and bHLH-PAS protein domains

Zhi Wang, Charles E. Smith, William R. Atchley
2007 Mathematical Biosciences  
Furthermore, it was found that the local amplitude minimum or local amplitude maximum of the 3.6-aa periodic component is associated with protein structural or functional information due to the observation  ...  that the extrema are mainly located in the boundary area of two structural or functional regions.  ...  This research was supported by a grant from the National Institutes of Health (GM45344) to W.R.A.  ... 
doi:10.1016/j.mbs.2007.01.004 pmid:17374384 fatcat:t5l6sqnttvenvoi73poj5hnnu4

Generalized Maximum Causal Entropy for Inverse Reinforcement Learning [article]

Tien Mai and Kennard Chan and Patrick Jaillet
2020 arXiv   pre-print
Motivated by a limitation of the classical maximum entropy model in capturing the structure of the network of states, we propose an IRL model based on a generalized version of the causal entropy maximization  ...  problem, which allows us to generate a class of maximum entropy IRL models.  ...  Conclusion In this work, we developed a generalized IRL model that is consistent with the principle of the maximum causal entropy framework and holds a worst-case prediction log-loss guarantee.  ... 
arXiv:1911.06928v2 fatcat:em6lejcsnrf2lbssnbiqahycwy
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