Auto-encoder neural network based prediction of Texas poker opponent's behavior

Combining autoencoder neural network and Bayesian inversion to estimate heterogeneous permeability distributions in enhanced geothermal reservoir: Model development and verification

A novel damage detection approach based on Auto-encoder neural network is proposed to identify damage in beam-like bridges subjected to a moving mass. In this approach, several sensors are used to measure structural vibration responses during a mass moving across the bridge. An auto-encoder (AE) neural network is designed to extract features from the measured responses. A fixed moving window is used to cut out the time-domain responses to generate inputs of the AE neural network. Moreover, some constraints are applied on the hidden layer to improve the performance of the AE network in training process. When the training is complete, the encoder was regarded as a feature extractor. And the damage index is defined as the cosine distance between two feature vectors obtained from adjacent data windows. By moving the window along the measured vibration data, we can calculate a damage index series and locate the damage position of the structure. To demonstrate the performance of the proposed method, numerical simulation is carried out. The results show that the proposed method can accurately locate both single and multiple damages using acceleration response. It infers the proposed method is promising for structural damage detection.

Imperfect information game in multiplayer no-limit Texas Hold'em is a critical challenge in AI research. Recent advanced solving approaches, such as deep CounterFactual Value networks(CFVnet) combined with continual resolving, provide a way to conduct depth-limited search in imperfect-information games. However, CFVnet has limited deployment in Heads-Up No-Limit Texas Hold'em, and is hard to scale to multiplayer setting. In this paper, we propose a novel algorithm, mean approximation, that effectively converting multi-agent interactions to two-agent interactions, and introduce a useful trick virtual action generation to solve conflicts occur in this conversion. Furthermore, we introduce several improvements to deep CFVnet applied in Texas Hold'em Poker. We combined all above improvements and extensions of CFVnet to create our poker AI MuCFVnet, unlocking the potential of deep CFVnet. Experimental results show that MuCFVnet has strong performance, successfully beats some available public multiplayer poker AI.

Poker has been gradually gaining the attention of the scientific community, mostly in researchers on Artificial Intelligence. The main reason is concerned with the fact that Poker provides great challenges to the research in the area. Unlike many other games, poker is a stochastic game of imperfect information, which creates a high amount of possibilities to every state of the game. In this work a different line of thought is followed by trying to create an agent capable of reproducing the way a professional Poker human player plays for all stages in a Texas Hold'em Poker game. For this purpose, a high level data model able to comprehend the maximum of information relevant to every state of the game was built, loaded with data from a database containing millions of plays made by a professional poker players, by using Talend Data Integration. To execute Data mining techniques Weka software package was used. The final results show that it is possible to create a virtual poker player that make very similar decisions of a professional poker player.

Imperfect information facilitates the evolution of reciprocity

In this paper a new algorithm for prediction opponent move in Texas Hold’em Poker game is presented. The algorithm is based on artificial intelligence approach – it uses several neural networks, each trained on a specific dataset. The results given by algorithm may be applied to improve players’ game. Moreover, the algorithm may be used as a part of more complex algorithm created for supporting decision making in Texas Hold’em Poker.

Linear spectral unmixing is an essential technique in hyperspectral image (HSI) processing and interpretation. In recent years, deep learning-based approaches have shown great promise in hyperspectral unmixing (HU), in particular, unsupervised unmixing methods based on autoencoder (AE) networks are a recent trend. The AE model, which automatically learns low-dimensional representations (abundances) and reconstructs data with their corresponding bases (endmembers), has achieved superior performance in HU. In this article, we explore the effective utilization of spatial and spectral information in AE-based unmixing networks. Important findings on the use of spatial and spectral information in the AE framework are discussed. Inspired by these findings, we propose a spatial–spectral collaborative unmixing network, called SSCU-Net, which learns a spatial AE network and a spectral AE network in an end-to-end manner to more effectively improve the unmixing performance. SSCU-Net is a two-stream deep network and shares an alternating architecture, where the two AE networks are efficiently trained in a collaborative way for estimation of endmembers and abundances. Meanwhile, we propose a new spatial AE network by introducing a superpixel segmentation method based on abundance information, which greatly facilitates the employment of spatial information and improves the accuracy of unmixing network. Moreover, extensive ablation studies are carried out to investigate the performance gain of SSCU-Net. Experimental results on both synthetic and real hyperspectral datasets illustrate the effectiveness and competitiveness of the proposed SSCU-Net compared with several state-of-the-art HU methods.

Autoencoder asset pricing models

Previous research has shown that online poker players may be at a high risk of developing gambling problems and that there are various personality traits that may be associated with such risk. An online survey comprising 256 poker players (50.8 % professional players and 49.2 % recreational players) examined various aspects of their poker playing behavior. The goals of the study were to investigate: (i) the playing motivations of recreational and professional Texas Hold’em poker players, (ii) the psychological characteristics of online poker players in relation to narcissism, impulsivity, self-esteem, and problem gambling, and (iii) the differences between professional and recreational poker player on these psychological variables. Results showed that only 1.6 % poker players met the DSM-IV-TR diagnostic criteria for pathological gambling (n = 4). Generally, poker players had low scores on narcissism and impulsivity, and high scores on self-esteem. Implications for the study of online poker gambling are also discussed.

Texas Hold'em has received considerable attention from researchers as a typical representative of imperfect information games. In the last 5 years, Texas Hold'em artificial intelligence (AI), which adopts algorithmic game theory as its core technology, has achieved many breakthroughs, with Libratus, DeepStack, Pluribus, and other Texas Hold'em AIs achieving the ability to beat top human players in heads-up and multiplayer no-limit Texas Hold'em. In the past 2 years, researchers have begun exploring the use of neural networks to build decision-making models to reduce the dependence of model training on expert knowledge. AlphaHoldem is an essential representative of these neural networks, beating Slumbot through end-to-end neural networks. However, AlphaHoldem does not fully consider game rules and other game information, and thus, the model's training relies on a large number of sampling and massive samples, making its training process considerably complicated. In this study, we propose DeepHoldem, an efficient end-to-end Texas Hold'em AI that combines algorithmic game theory and game information. DeepHoldem uses reinforcement learning to simulate the self-play process of the counterfactual regret minimization algorithm. DeepHoldem can efficiently converge to the Nash equilibrium by considering player and opponent hand information when encoding the game state and incorporating the attention module into the network structure to achieve the correlation extraction of game action sequences, and thus, fully utilize game state information. In addition, DeepHoldem incorporates the concept of supervised learning into the training process, dramatically improving the training efficiency of the model and expanding its application scope. Experimental results show that after about 4 days of training, DeepHoldem beats Slumbot at a level of 2.1 mbb/h, and the single decision time is less than 0.1 seconds.

Imbalanced data classification problem has always been a hotspot in the field of machine learning research. Pointing to the overfitting and noise problems of oversampling algorithm when synthesizing new minority class samples, the current study proposed a stacked denoising autoencoder neural network (SDAE) algorithm based on cost-sensitive oversampling, combining the cost-sensitive learning with denoising autoencoder neural network. The proposed algorithm can not only oversample minority class sample through misclassification cost, but it can denoise and classify the sampled dataset. Experiment shows that, compared with the traditional stacked autoencoder neural network (SAE) and oversampling autoencoder neural network without denoising process (OS-SAE), the proposed algorithm improves the classification accuracy of minority class of imbalanced datasets.

In order to improve the efficiency and accuracy of diesel engine combustion chamber fault isolation, a method of combining the feature dimension reduction of AutoEncoder network and the fault isolation of BP neural network was proposed based on acoustic emission signals. Taking a Z6170 diesel engine of China ZICHAI company as an example, fault simulation tests of exhaust valve and piston rings under experimental environments were carried out, and the acoustic emission signals of the cylinder head were collected, then the time-domain, frequency-domain and other characteristic parameters of different signal sections in the whole cycle were extracted. The dimension of characteristic parameters was reduced by using AutoEncoder network, BP neural network was used to fault diagnosis and fault isolation, so that a fault diagnosis and fault isolation model of combustion chamber components was established. After training and verification of the model, it shows that the proposed diagnosis and isolation method is effective with capability of identifying the faults of exhaust valve and piston ring for the combustion chamber parts of diesel engines, therefore, it is promising to detect and isolate the condition of combustion components automatically.KeywordsDiesel engineFault diagnosisAutoEncoder networkBP neural networkAcoustic emission

ADHD classification using auto-encoding neural network and binary hypothesis testing

BackgroundMicrobes have dense linkages with human diseases. Balanced microorganisms protect human body against physiological disorders while unbalanced ones may cause diseases. Thus, identification of potential associations between microbes and diseases can contribute to the diagnosis and therapy of various complex diseases. Biological experiments for microbe–disease association (MDA) prediction are expensive, time-consuming, and labor-intensive.MethodsWe developed a computational MDA prediction method called GPUDMDA by combining graph attention autoencoder, positive-unlabeled learning, and deep neural network. First, GPUDMDA computes disease similarity and microbe similarity matrices by integrating their functional similarity and Gaussian association profile kernel similarity, respectively. Next, it learns the feature representation of each microbe–disease pair using graph attention autoencoder based on the obtained disease similarity and microbe similarity matrices. Third, it selects a few reliable negative MDAs based on positive-unlabeled learning. Finally, it takes the learned MDA features and the selected negative MDAs as inputs and designed a deep neural network to predict potential MDAs.ResultsGPUDMDA was compared with four state-of-the-art MDA identification models (i.e., MNNMDA, GATMDA, LRLSHMDA, and NTSHMDA) on the HMDAD and Disbiome databases under five-fold cross validations on microbes, diseases, and microbe-disease pairs. Under the three five-fold cross validations, GPUDMDA computed the best AUCs of 0.7121, 0.9454, and 0.9501 on the HMDAD database and 0.8372, 0.8908, and 0.8948 on the Disbiome database, respectively, outperforming the other four MDA prediction methods. Asthma is the most common chronic respiratory condition and affects ~339 million people worldwide. Inflammatory bowel disease is a class of globally chronic intestinal disease widely existed in the gut and gastrointestinal tract and extraintestinal organs of patients. Particularly, inflammatory bowel disease severely affects the growth and development of children. We used the proposed GPUDMDA method and found that Enterobacter hormaechei had potential associations with both asthma and inflammatory bowel disease and need further biological experimental validation.ConclusionThe proposed GPUDMDA demonstrated the powerful MDA prediction ability. We anticipate that GPUDMDA helps screen the therapeutic clues for microbe-related diseases.

Unsupervised anomaly detection in pressurized water reactor digital twins using autoencoder neural networks