scholarly journals A Study of Prisoner’s Dilemma Game Model with Incomplete Information

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Xiuqin Deng ◽  
Jiadi Deng
Keyword(s):  
Incomplete Information ◽  
Hash Table ◽  
Game Problem ◽  
Multiplayer Games ◽  
Total Income ◽  
Bayes Model ◽  
Bayes Formula ◽  
Machine Learning Model ◽  
Prisoners Dilemma ◽  
Novel Method

Prisoners’ dilemma is a typical game theory issue. In our study, it is regarded as an incomplete information game with unpublicized game strategies. We solve our problem by establishing a machine learning model using Bayes formula. The model established is referred to as the Bayes model. Based on the Bayesian model, we can make the prediction of players’ choices to better complete the unknown information in the game. And we suggest the hash table to make improvement in space and time complexity. We build a game system with several types of game strategy for testing. In double- or multiplayer games, the Bayes model is more superior to other strategy models; the total income using Bayes model is higher than that of other models. Moreover, from the result of the games on the natural model with Bayes model, as well as the natural model with TFT model, it is found that Bayes model accrued more benefits than TFT model on average. This demonstrates that the Bayes model introduced in this study is feasible and effective. Therefore, it provides a novel method of solving incomplete information game problem.

A Novel Amino Acid Sequence-based Computational Approach to Predicting Cell-penetrating Peptides

2019 ◽  
Vol 15 (3) ◽  
pp. 206-211 ◽  
Author(s):  
Jihui Tang ◽  
Jie Ning ◽  
Xiaoyan Liu ◽  
Baoming Wu ◽  
Rongfeng Hu
Keyword(s):  
Machine Learning ◽  
Amino Acid ◽  
Amino Acid Position ◽  
Cell Penetrating Peptides ◽  
Support Vector ◽  
Cell Penetration ◽  
Drug Candidates ◽  
Machine Learning Model ◽  
Cell Penetrating ◽  
Novel Method

<P>Introduction: Machine Learning is a useful tool for the prediction of cell-penetration compounds as drug candidates. </P><P> Materials and Methods: In this study, we developed a novel method for predicting Cell-Penetrating Peptides (CPPs) membrane penetrating capability. For this, we used orthogonal encoding to encode amino acid and each amino acid position as one variable. Then a software of IBM spss modeler and a dataset including 533 CPPs, were used for model screening. </P><P> Results: The results indicated that the machine learning model of Support Vector Machine (SVM) was suitable for predicting membrane penetrating capability. For improvement, the three CPPs with the most longer lengths were used to predict CPPs. The penetration capability can be predicted with an accuracy of close to 95%. </P><P> Conclusion: All the results indicated that by using amino acid position as a variable can be a perspective method for predicting CPPs membrane penetrating capability.</P>

scholarly journals Two-Stage Pursuit Strategy for Incomplete-Information Impulsive Space Pursuit-Evasion Mission Using Reinforcement Learning

Aerospace ◽  
2021 ◽  
Vol 8 (10) ◽  
pp. 299
Author(s):  
Bin Yang ◽  
Pengxuan Liu ◽  
Jinglang Feng ◽  
Shuang Li
Keyword(s):  
Reinforcement Learning ◽  
Incomplete Information ◽  
Trajectory Optimization ◽  
Game Problem ◽  
Gradient Algorithm ◽  
Two Stage ◽  
Pursuit Evasion ◽  
Evasion Game ◽  
Close Distance ◽  
Pursuit Strategy

This paper presents a novel and robust two-stage pursuit strategy for the incomplete-information impulsive space pursuit-evasion missions considering the J2 perturbation. The strategy firstly models the impulsive pursuit-evasion game problem into a far-distance rendezvous stage and a close-distance game stage according to the perception range of the evader. For the far-distance rendezvous stage, it is transformed into a rendezvous trajectory optimization problem and a new objective function is proposed to obtain the pursuit trajectory with the optimal terminal pursuit capability. For the close-distance game stage, a closed-loop pursuit approach is proposed using one of the reinforcement learning algorithms, i.e., the deep deterministic policy gradient algorithm, to solve and update the pursuit trajectory for the incomplete-information impulsive pursuit-evasion missions. The feasibility of this novel strategy and its robustness to different initial states of the pursuer and evader and to the evasion strategies are demonstrated for the sun-synchronous orbit pursuit-evasion game scenarios. The results of the Monte Carlo tests show that the successful pursuit ratio of the proposed method is over 91% for all the given scenarios.

scholarly journals Study on the Strategy of Playing Doudizhu Game Based on Multirole Modeling

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Shuqin Li ◽  
Saisai Li ◽  
Hengyang Cao ◽  
Kun Meng ◽  
Meng Ding
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Decision Making ◽  
Incomplete Information ◽  
Computer Game ◽  
Game Problem ◽  
Role Modeling ◽  
Human Beings ◽  
National Competition ◽  
Evaluation Algorithm

Doudizhu poker is a very popular and interesting national poker game in China, and now it has become a national competition in China. As this game is a typical example of incomplete information game problem, it has received more and more attention from artificial intelligence experts. This paper proposes a multirole modeling-based card-playing framework. This framework includes three parts: role modeling, cards carrying, and decision-making strategies. Role modeling learns different roles and behaviors by using a convolutional neural network. Cards carrying can calculate reasonable rules especially for “triplet” by using an evaluation algorithm. Decision making is for implementing different card strategies for different player roles. Experimental results showed that this card-playing framework makes playing decisions like human beings, and it can to some extent learn, collaborate, and reason when facing an incomplete information game problem. This framework won the runner-up in the 2018 China Computer Game Competition.

scholarly journals Mapping Global Environmental Suitability for Sorghum bicolor (L.) Moench

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1928 ◽  
Author(s):  
Dong Jiang ◽  
Tian Ma ◽  
Fangyu Ding ◽  
Jingying Fu ◽  
Mengmeng Hao ◽  
...  
Keyword(s):  
Sorghum Bicolor ◽  
Sweet Sorghum ◽  
Regional Scale ◽  
Predictive Performance ◽  
Marginal Lands ◽  
Global Environmental ◽  
Machine Learning Model ◽  
Sample Data ◽  
Novel Method ◽  
Sorghum Bicolor L

Sorghum bicolor (L.) Moench, called sweet sorghum, is a drought-resistant and heat-tolerant plant used for ethanol bioenergy production, and is able to reduce the competition between growing crops for energy vs. growing crops for food. Quantitatively mapping the marginal lands of sweet sorghum is essential for the development of sorghum-based fuel ethanol production. However, knowledge of the contemporary marginal lands of sweet sorghum remains incomplete, and usually relies on sample data or is evaluated at a national or regional scale based on established rules. In this study, a novel method was demonstrated for mapping the global marginal lands of sweet sorghum based on a machine learning model. The total amount of global marginal lands suitable for sweet sorghum is 4802.21 million hectares. The model was applied to training and validation samples, and achieved high predictive performance, with the area under the receiver operating characteristic (ROC) curve (AUC) values of 0.984 and 0.978, respectively. In addition, the results illustrate that maximum annual temperature contributes more than do other variables to the predicted distribution of sweet sorghum and has a contribution rate of 40.2%.

scholarly journals Votes on Twitter: Assessing Candidate Preferences and Topics of Discussion During the 2016 U.S. Presidential Election

SAGE Open ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 215824401879165 ◽  
Author(s):  
Anjie Fang ◽  
Philip Habel ◽  
Iadh Ounis ◽  
Craig MacDonald
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Presidential Election ◽  
Presidential Candidates ◽  
Bayes Model ◽  
Twitter Data ◽  
Accuracy And Precision ◽  
Machine Learning Model ◽  
2016 Election ◽  
High Degree

Social media offers scholars new and innovative ways of understanding public opinion, including citizens’ prospective votes in elections and referenda. We classify social media users’ preferences over the two U.S. presidential candidates in the 2016 election using Twitter data and explore the topics of conversation among proClinton and proTrump supporters. We take advantage of hashtags that signaled users’ vote preferences to train our machine learning model which employs a novel classifier—a Topic-Based Naive Bayes model—that we demonstrate improves on existing classifiers. Our findings demonstrate that we are able to classify users with a high degree of accuracy and precision. We further explore the similarities and divergences among what proClinton and proTrump users discussed on Twitter.

scholarly journals Exploring the Machine Learning Algorithm for Prediction the Loan Sanctioning Process

2019 ◽  
Vol 9 (1) ◽  
pp. 2714-2719
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Accurate Result ◽  
Learning Algorithm ◽  
Experimental Tests ◽  
Safe Procedure ◽  
Bayes Model ◽  
The Past ◽  
Machine Learning Model ◽  
Number Of Customers

Extending credits to corporates and individuals for the smooth functioning of growing economies like India is inevitable. As increasing number of customers apply for loans in the banks and non- banking financial companies (NBFC), it is really challenging for banks and NBFCs with limited capital to device a standard resolution and safe procedure to lend money to its borrowers for their financial needs. In addition, in recent times NBFC inventories have suffered a significant downfall in terms of the stock price. It has contributed to a contagion that has also spread to other financial stocks, adversely affecting the benchmark in recent times. In this paper, an attempt is made to condense the risk involved in selecting the suitable person who could repay the loan on time thereby keeping the bank’s non-performing assets (NPA) on the hold. This is achieved by feeding the past records of the customer who acquired loans from the bank into a trained machine learning model which could yield an accurate result. The prime focus of the paper is to determine whether or not it will be safe to allocate the loan to a particular person. This paper has the following sections (i) Collection of Data, (ii) Data Cleaning and (iii) Performance Evaluation. Experimental tests found that the Naïve Bayes model has better performance than other models in terms of loan forecasting.

scholarly journals Machine Learning Model for Prediction of Diabetes Mellitus

2020 ◽  
Vol 8 (5) ◽  
pp. 2376-2381
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Learning Model ◽  
Endocrine Disease ◽  
Machine Learning Model ◽  
Novel Method ◽  
Prediction Of Diabetes ◽  
Accuracy Of Prediction

Today world is extensively affected by endocrine disease Diabetes Mellitus which is commonly known as diabetes. There is a need for an effective model which can predict diabetes and its types at the early stages with accuracy. To improve the accuracy of prediction and to achieve better efficiency, a new Machine Learning based Model (MLM) is proposed. This Machine Learning Model (MLM) has ability to predict the diabetes and its categories as type 1, type 2 and Gestational diabetic with which the patient is suffering from. The proposed Machine Learning Model is innovative for diagnosis of diabetes is more accurate as compared to other existing approaches.This is a novel method from which one can combine power of an expert system with the machine learning environment.

scholarly journals A Machine Learning Approach for Predicting Student Performance

2019 ◽  
pp. 462-465
Author(s):  
C. Selvi ◽  
R. Shalini ◽  
V. Navaneethan ◽  
L. Santhiya
Keyword(s):  
Machine Learning ◽  
Academic Performance ◽  
Student Performance ◽  
Learning Model ◽  
Learning Approach ◽  
Economic Prosperity ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Novel Method ◽  
Predicting Student Performance

An University’s reputation and its standard are weighted by its students performance and their part in the future economic prosperity of the nation, hence a novel method of predicting the student’s upcoming academic performance is really essential to provide a pre-requisite information upon their performances. A machine learning model can be developed to predict the student’s upcoming scores or their entire performance depending upon their previous academic performances.

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