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

Maryam Ghasemi; Abdolreza  Roshani; Peshawa J. Muhammad Ali; Farhad F. Nia; Ehsan Nazemi; Gholam H. Roshani

doi:10.14500/aro.10757

Maryam Ghasemi Department of Electrical Engineering, Faculty of Energy, Kermanshah University of Technology, Kermanshah, Iran https://orcid.org/0000-0002-0098-1748
Abdolreza Roshani Department of Industrial Engineering, Faculty of Engineering Management, Kermanshah University of Technology, Kermanshah, Iran http://orcid.org/0000-0002-3067-0633
Peshawa J. Muhammad Ali Department of Software Engineering, Faculty of Engineering, Koya University, Koya KOY45, Kurdistan Region – F.R. Iraq https://orcid.org/0000-0002-0471-5172
Farhad F. Nia Department of Electrical Engineering, Faculty of Energy, Kermanshah University of Technology, Kermanshah, Iran.
Ehsan Nazemi Imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium. https://orcid.org/0000-0001-5457-6943
Gholam H. Roshani Department of Electrical Engineering, Faculty of Energy, Kermanshah University of Technology, Kermanshah, Iran https://orcid.org/0000-0002-3328-1502

Keywords: Multilayer perceptron, Radial basis functions, upgraded Markov model, Rock, Paper, Scissors game

Abstract

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.

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Author Biographies

Maryam Ghasemi, Department of Electrical Engineering, Faculty of Energy, Kermanshah University of Technology, Kermanshah, Iran

Maryam Ghasemi is an M.Sc. student at the Department of Electrical Engineering, Faculty of Energy, Kermanshah University of Technology, Iran. She got the B.Sc. degree in Telecommunications Engineering. Her research interests are in artificial intelligence and machine learning

Abdolreza Roshani, Department of Industrial Engineering, Faculty of Engineering Management, Kermanshah University of Technology, Kermanshah, Iran

Abdolreza Roshani is an Assistant Professor at the Department of Industrial Engineering, Faculty of Engineering, Kermanshah University of Technology. He got a B.Sc. degree in Applied Mathematics, and M.Sc. and Ph.D. degrees in Industrial Engineering. His research interests are in meta-heuristic algorithms, mathematical programming, and AI applications. His research topics include production planning and scheduling, assembly systems analysis and design, sequencing problems, and forecasting.

Peshawa J. Muhammad Ali, Department of Software Engineering, Faculty of Engineering, Koya University, Koya KOY45, Kurdistan Region – F.R. Iraq

Peshawa Muhammad Ali is a Lecturer at the Department of Software Engineering, Faculty of Engineering, Koya University. He got a B.Sc. degree in Civil Engineering and an M.Sc. degree in Computer Science. His research interests are in machine learning, deep learning, and artificial neural network. Peshawa is a member of Kurdistan Engineers Sandycate.

Farhad F. Nia, Department of Electrical Engineering, Faculty of Energy, Kermanshah University of Technology, Kermanshah, Iran.

Farhad Fouladinia is a Lecturer at the Department of Electrical Engineering, Faculty of Energy, Kermanshah University of Technology, Iran. He got the B.Sc. degree in Telecommunications Engineering and the M.Sc. degree in Nano Electronic at KUT. His research interests are in Quantum Dot Cellular Automata (QCA), microstrip filters and application of artificial intelligence in various fields. He also interests to design and fabricate physical gaming devices.

Ehsan Nazemi, Imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium.

Ehsan Nazemi is a postdoctoral researcher at the Department of Physics, Faculty of Science, Antwerp University, Belgium. He got the B.Sc. degree in Physics, the M.Sc. degree in Applied Physics and the Ph.D. degree in Applied Physics. His research interests are in X-ray imaging, multiphase flowmeters and application of artificial intelligence in different engineering problems. Dr. Ehsan Nazemi is a member of editorial board of Fluids and Radiation journals from MDPI publications.

Gholam H. Roshani, Department of Electrical Engineering, Faculty of Energy, Kermanshah University of Technology, Kermanshah, Iran

Gholam Hossein Roshani is an Associate Prof. at the Department of Electrical Engineering, Faculty of Energy, Kermanshah University of Technology, Iran. He got the B.Sc. degree in Electrical Engineering, the M.Sc. degree in Applied Physics and the Ph.D. degree in Applied Physics. His research interests are in artificial intelligence, multiphase flowmeters, cognitive games and application of artificial intelligence in different engineering problems.

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