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

In this paper, the implementation of artificial neural networks (multilayer perceptron [MLP] and radial base functions [RBF]) upgraded Markov chain model have been studied performed to identify human behavior patterns during rock, scissors game. The main motivation research is design construction an intelligent robot with ability defeat a opponent. MATLAB software has used implement algorithms. After implementing algorithms, their effectiveness in detecting pattern investigated. To ensure ideal performance implemented model, each player played desired algorithms three different stages. results showed that percentage winning computer MLP RBF on average men women 59%, 76.66%, 75%, respectively. Obtained clearly indicate very good network mental modeling opponent game scissors. end, designed employed both hardware which include Zana digital version graphical user interface stand. best knowledge authors, precision novel presented method for determining was highest among all previous studies.