Design and Construction of Zana Robot for Modeling Human Player in Rock-paper-scissors Game using Multilayer Perceptron, Radial basis Functions and Markov Algorithms

ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY

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Field Value
 
Title Design and Construction of Zana Robot for Modeling Human Player in Rock-paper-scissors Game using Multilayer Perceptron, Radial basis Functions and Markov Algorithms
 
Creator Ghasemi, Maryam
Roshani, Abdolreza
Muhammad Ali, Peshawa J.
Nia, Farhad F.
Nazemi, Ehsan
Roshani, Gholam H.
 
Subject Multilayer perceptron
Radial basis functions
upgraded Markov model
Rock
Paper
Scissors game
 
Description In this paper, the implementation of artificial neural networks (multilayer perceptron [MLP] and radial base functions [RBF]) and the upgraded Markov chain model have been studied and performed to identify the human behavior patterns during rock, paper, and scissors game. The main motivation of this research is the design and construction of an intelligent robot with the ability to defeat a human opponent. MATLAB software has been used to implement intelligent algorithms. After implementing the algorithms, their effectiveness in detecting human behavior pattern has been investigated. To ensure the ideal performance of the implemented model, each player played with the desired algorithms in three different stages. The results showed that the percentage of winning computer with MLP and RBF neural networks and upgraded Markov model, on average in men and women is 59%, 76.66%, and 75%, respectively. Obtained results clearly indicate a very good performance of the RBF neural network and the upgraded Markov model in the mental modeling of the human opponent in the game of rock, paper, and scissors. In the end, the designed game has been employed in both hardware and software which include the Zana intelligent robot and a digital version with a graphical user interface design on the stand. To the best knowledge of the authors, the precision of novel presented method for determining human behavior patterns was the highest precision among all of the previous studies.
 
Publisher Koya University
 
Date 2021-03-08
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed article
text
 
Format application/pdf
 
Identifier http://aro.koyauniversity.org/index.php/aro/article/view/757
10.14500/aro.10757
 
Source ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY; Vol. 9 No. 1 (2021): Issue Sixteen; 67-76
2307-549X
2410-9355
 
Language eng
 
Relation http://aro.koyauniversity.org/index.php/aro/article/view/757/208
 
Rights Copyright (c) 2021 Maryam Ghasemi, Abdolreza Roshani, Peshawa J. Muhammad Ali, Farhad F. Nia, Ehsan Nazemi, Gholam H. Roshani
https://creativecommons.org/licenses/by-nc-sa/4.0
 

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