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.