In this chapter, we discussed how to add Artificial Intelligence to ChefBot in order to interact with people. This function is an add-on to ChefBot to increase the interactivity of the robot. We used simple AI techniques such as pattern matching and searching in ChefBot. The pattern datasets are stored in a special type of file called AIML. The Python interpreter module is called PyAIML. We used this to decode AIML files. The user can store the pattern data in an AIML format and PyAIML can interpret this pattern. This method is similar to a stimulus-response system. The user has to give a stimulus in the form of text data and from the AIML pattern, the module finds the appropriate reply to the user input. We saw the entire communication system of the robot and how the robot communicates with people. It includes speech recognition and synthesis along with AI. We already discussed speech in the previous chapter. We also saw useful tags used in AIML and the PyAIML installation, how they work, and some examples. Finally, we implemented the entire code in ROS along with the speech recognition and synthesis units. In the next chapter, we will discuss the integration of components in the robot, which we have not discussed until now.