Designing and building a dialogue mechanism suitable for RoboPuppet using deep learning

The purpose of this research is to design and build a puppet robot with the capability of deep conversational learning with a deep reasoning approach. Due to the mentioned advances in artificial intelligence and deep learning, it is possible to provide conditions where the puppeteer's dependence on the handler is reduced and communicate with the audience intelligently. This robot, by recognizing the Farsi speech of the audience, determines a suitable answer to the question and broadcasts it in Persian language. The importance of this issue is in creating a suitable dialogue mechanism. This mechanism is a deep learning algorithm that by recognizing the question posed by the user, provides a set of possibilities from the categories included in the robot's dataset, and considering the highest probability, the desired category in which the user's question is placed is determined, and among the answers For that category of questions, an answer is chosen randomly. In addition, the puppet robot dialogue mechanism has a few simple conditional sections that can provide appropriate answers to repetitive or obscure questions. In the results of various trainings, by changing the parameters in the deep learning model of this robot with a 64-class dataset, it was found that the use of crowded layers with many neurons works better than a few layers of them,