Using Convolution and Deep Learning in Gomoku Game Artificial Intelligence

2018 ◽  
Vol 28 (03) ◽  
pp. 1850011
Author(s):  
Peizhi Yan ◽  
Yi Feng
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Network Model ◽  
Neural Network Model ◽  
Traditional Approach ◽  
Training Data ◽  
Game Tree ◽  
The Neural Network ◽  
Testing Data ◽  
Game Board

Gomoku is an ancient board game. The traditional approach to solving the Gomoku game is to apply tree search on a Gomoku game tree. Although the rules of Gomoku are straightforward, the game tree complexity is enormous. Unlike many other board games such as chess and Shogun, the Gomoku board state is more intuitive. That is to say, analyzing the visual patterns on a Gomoku game board is fundamental to play this game. In this paper, we designed a deep convolutional neural network model to help the machine learn from the training data (collected from human players). Based on this original neural network model, we made some changes and get two variant neural networks. We compared the performance of the original neural network with its variants in our experiments. Our original neural network model got 69% accuracy on the training data and 38% accuracy on the testing data. Because the decision made by the neural network is intuitive, we also designed a hard-coded convolution-based Gomoku evaluation function to assist the neural network in making decisions. This hybrid Gomoku artificial intelligence (AI) further improved the performance of a pure neural network-based Gomoku AI.

scholarly journals A Graph Neural Network for Predicting Energy and Stability of Known and Hypothetical Crystal Structures

2021 ◽  
Author(s):  
Shubham Pandey ◽  
Jiaxing Qu ◽  
Vladan Stevanovic ◽  
Peter St. John ◽  
Prashun Gorai
Keyword(s):  
Neural Network ◽  
Total Energy ◽  
Network Model ◽  
Neural Network Model ◽  
Density Functional ◽  
Failure Modes ◽  
Inorganic Materials ◽  
Training Data ◽  
The Neural Network ◽  
Unique Graph

The discovery of new inorganic materials in unexplored chemical spaces necessitates calculating total energy quickly and with sufficient accuracy. Machine learning models that provide such a capability for both ground-state (GS) and higher-energy structures would be instrumental in accelerating the screening for new materials over vast chemical spaces. Here, we develop a unique graph neural network model to accurately predict the total energy of both GS and higher-energy hypothetical structures. We use ~16,500 density functional theory calculated total energy from the NREL Materials Database and ~11,000 in-house generated hypothetical structures to train our model, thus making sure that the model is not biased towards either GS or higher-energy structures. We also demonstrate that our model satisfactorily ranks the structures in the correct order of their energies for a given composition. Furthermore, we present a thorough error analysis to explain several failure modes of the model, which highlights both prediction outliers and occasional inconsistencies in the training data. By peeling back layers of the neural network model, we are able to derive chemical trends by analyzing how the model represents learned structures and properties.

Fracture Design Parameters of Middle Carbon Steel in Extra-Low Cycle Bend Torsion Loading

2014 ◽  
Vol 574 ◽  
pp. 342-346
Author(s):  
Hong Yan Duan ◽  
Huan Rong Zhang ◽  
Ming Zheng ◽  
Xiao Hong Wang
Keyword(s):  
Neural Network ◽  
Carbon Steel ◽  
Network Model ◽  
Neural Network Model ◽  
Network Architecture ◽  
Training Data ◽  
Design Parameters ◽  
Ann Model ◽  
Approximate Methods ◽  
The Neural Network

The fracture problems of medium carbon steel under extra-low cycle bend torsion fatigue loading were studied using artificial neural networks (ANN) in this paper. The ANN model exhibited excellent comparison with the experimental results. It was concluded that predicted fracture design parameters by the trained neural network model seem more reasonable compared to approximate methods. It is possible to claim that, ANN is fairly promising prediction technique if properly used. Training ANN model was introduced at first. And then the Training data for the development of the neural network model was obtained from the experiments. The input parameters, the presetting deflection and notch open angle, and the output, the cycle times of fracture were used during the network training. The neural network architecture is designed. The ANN model was developed using back propagation architecture with three layers jump connections, where every layer was connected or linked to every previous layer. The number of hidden neurons was determined according to special formula. The performance of system is summarized at last. In order to facilitate the comparisons of predicted values, the error evaluation and mean relative error are obtained. The result show that the training model has good performance, and the experimental data and predicted data from ANN are in good coherence.

Application of ANN Back-Propagation for Fracture Design Parameters of Middle Carbon Steel in Extra-Low Cycle Bend Torsion Loading

2013 ◽  
Vol 345 ◽  
pp. 272-276 ◽  
Author(s):  
Hong Yan Duan ◽  
You Tang Li ◽  
Zhi Jia Sun ◽  
Yang Yang Zhang
Keyword(s):  
Neural Network ◽  
Carbon Steel ◽  
Network Model ◽  
Neural Network Model ◽  
Network Architecture ◽  
Back Propagation ◽  
Training Data ◽  
Design Parameters ◽  
Ann Model ◽  
The Neural Network

The fracture problems of medium carbon steel (MCS) under extra-low cycle bend torsion loading were studied using artificial neural networks (ANN) in this paper. The training data were used in the formation of training set of ANN. The ANN model exhibited excellent comparison with the experimental results. It was concluded that predicted fracture design parameters by the trained neural network model seem more reasonable compared to approximate methods. It is possible to claim that, ANN is fairly promising prediction technique if properly used. Training ANN model was introduced at first. And then the Training data for the development of the neural network model was obtained from the experiments. The input parameters, notch depth and tip radius of the notch, and the output, the cycle times of fracture were used during the network training. The neural network architecture is designed. The ANN model was developed using back propagation architecture with three layers jump connections, where every layer was connected or linked to every previous layer. The number of hidden neurons was determined according to special formula. The performance of system is summarized at last. In order to facilitate the comparisons of predicted values, the error evaluation and mean relative error are obtained. The result show that the training model has good performance, and the experimental data and predicted data from ANN are in good coherence.

scholarly journals A Novel Neural Network Model based on Cerebral Hemispheric Asymmetry for EEG Emotion Recognition

2018 ◽  
Author(s):  
Yang Li ◽  
Wenming Zheng ◽  
Zhen Cui ◽  
Tong Zhang ◽  
Yuan Zong
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Network Model ◽  
Neural Network Model ◽  
Training Data ◽  
Local Domain ◽  
Data Representations ◽  
Testing Data ◽  
Left And Right ◽  
Class Labels

In this paper, we propose a novel neural network model, called bi-hemispheres domain adversarial neural network (BiDANN), for EEG emotion recognition. BiDANN is motivated by the neuroscience findings, i.e., the emotional brain's asymmetries between left and right hemispheres. The basic idea of BiDANN is to map the EEG feature data of both left and right hemispheres into discriminative feature spaces separately, in which the data representations can be classified easily. For further precisely predicting the class labels of testing data, we narrow the distribution shift between training and testing data by using a global and two local domain discriminators, which work adversarially to the classifier to encourage domain-invariant data representations to emerge. After that, the learned classifier from labeled training data can be applied to unlabeled testing data naturally. We conduct two experiments to verify the performance of our BiDANN model on SEED database. The experimental results show that the proposed model achieves the state-of-the-art performance.

scholarly journals A Graph Neural Network for Predicting Energy and Stability of Known and Hypothetical Crystal Structures

2021 ◽  
Author(s):  
Shubham Pandey ◽  
Jiaxing Qu ◽  
Vladan Stevanovic ◽  
Peter St. John ◽  
Prashun Gorai
Keyword(s):  
Neural Network ◽  
Total Energy ◽  
Network Model ◽  
Neural Network Model ◽  
Density Functional ◽  
Failure Modes ◽  
Inorganic Materials ◽  
Training Data ◽  
The Neural Network ◽  
Unique Graph

The discovery of new inorganic materials in unexplored chemical spaces necessitates calculating total energy quickly and with sufficient accuracy. Machine learning models that provide such a capability for both ground-state (GS) and higher-energy structures would be instrumental in accelerating the screening for new materials over vast chemical spaces. Here, we develop a unique graph neural network model to accurately predict the total energy of both GS and higher-energy hypothetical structures. We use ~16,500 density functional theory calculated total energy from the NREL Materials Database and ~11,000 in-house generated hypothetical structures to train our model, thus making sure that the model is not biased towards either GS or higher-energy structures. We also demonstrate that our model satisfactorily ranks the structures in the correct order of their energies for a given composition. Furthermore, we present a thorough error analysis to explain several failure modes of the model, which highlights both prediction outliers and occasional inconsistencies in the training data. By peeling back layers of the neural network model, we are able to derive chemical trends by analyzing how the model represents learned structures and properties.

Application of ANN Back-Propagation for An Alloy Reinforced Ceramics/Metal Composite under Extra-Low Cycle Bending Fatigue Loading

2010 ◽  
Vol 105-106 ◽  
pp. 108-111
Author(s):  
Zhi Yuan Rui ◽  
Hong Yan Duan ◽  
Chun Li Lei ◽  
Xing Chun Wei
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Back Propagation ◽  
Propagation Model ◽  
Training Data ◽  
Design Parameters ◽  
Metal Composite ◽  
Ann Model ◽  
The Neural Network

Artificial neural network (ANN) back-propagation model was developed to predict the fracture design parameters in reinforced ceramic matrix composites (CMCS).Training ANN model was introduced at first. And then the Training data for the development of the neural network model was obtained from the experiments. The input parameters, the presetting deflection and tip radius of the notch, and the output, the cycle times of fracture were used during the network training. The neural network architecture is designed. The ANN model was developed using back propagation architecture with three layers jump connections, where every layer was connected or linked to every previous layer. The number of hidden neurons was determined according to special formula. The performance of system is summarized at last. The ANN model exhibited excellent comparison with the experimental results. It was concluded that predicted fracture design parameters by the trained neural network model seem more reasonable compared to approximate methods. It is possible to claim that, ANN is fairly promising prediction technique if properly used.

Artificial intelligence model for COVID-19 detection based on native chest computed tomography

MedAlliance ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 6-13
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Computed Tomography ◽  
Network Model ◽  
Neural Network Model ◽  
Chest Ct ◽  
Lung Damage ◽  
Data Set ◽  
The Neural Network ◽  
Coronavirus Infection

Summary The aim is to develop and evaluate the clinical effectiveness of the artificial intelligence system for analyzing chest CT images, recognizing the leading signs of lung damage caused by coronavirus infection, and determining the damage volume. Materials and methods. We used open source images for the model training and validation, including anonymized complete studies or separate axial sections of chest CT of patients with PCR confirmed coronavirus etiology of lung disease. Algorithm of the model included sequence as follows: axial sections of CT of the chest organs at the entrance, image segmentation into 4 classes, visualization and calculation of the area as a part (in%) occupied by each finding from the total area of the pulmonary fields in all sections available for analysis. In this work, we used a convolutional artificial neural network for segmentation of the form of an encoder-decoder, with a Feature Pyramid Network (FPN) decoder and an encoder based on the EfficientNet-B5 classification neural network. To increase the diversity of the training data set, as well as to protect the neural network model from retraining, input image transformations are used in the learning process. Results. The neural network model with high accuracy (IoU 0.82-0.97) reveals diagnostic signs that determine the severity of lung damage with a new coronavirus infection, on axial sections of native computed tomography of the chest. The diagnostic accuracy of the model for determining the signs of interstitial and alveolar infiltration exceeds the accuracy of a novice radiologist and is comparable to a diagnostician in predicting the shape of pulmonary fields and the presence of pleural effusion. Findings. The model can be used as an effective intelligent radiologist assistant when working with CT studies of patients with suspected coronavirus infection.

Estimating Traffic Volume on Minor Roads at Rural Stop-Controlled Intersections using Deep Learning

2019 ◽  
Vol 2673 (4) ◽  
pp. 108-116 ◽  
Author(s):  
Mostafa H. Tawfeek ◽  
Karim El-Basyouny
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Safety Management ◽  
Estimation Method ◽  
Estimation Model ◽  
The Neural Network ◽  
Annual Average Daily Traffic

Safety Performance Functions (SPFs) are regression models used to predict the expected number of collisions as a function of various traffic and geometric characteristics. One of the integral components in developing SPFs is the availability of accurate exposure factors, that is, annual average daily traffic (AADT). However, AADTs are not often available for minor roads at rural intersections. This study aims to develop a robust AADT estimation model using a deep neural network. A total of 1,350 rural four-legged, stop-controlled intersections from the Province of Alberta, Canada, were used to train the neural network. The results of the deep neural network model were compared with the traditional estimation method, which uses linear regression. The results indicated that the deep neural network model improved the estimation of minor roads’ AADT by 35% when compared with the traditional method. Furthermore, SPFs developed using linear regression resulted in models with statistically insignificant AADTs on minor roads. Conversely, the SPF developed using the neural network provided a better fit to the data with both AADTs on minor and major roads being statistically significant variables. The findings indicated that the proposed model could enhance the predictive power of the SPF and therefore improve the decision-making process since SPFs are used in all parts of the safety management process.

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