Logic-based machine comprehension for chatbots

We target here the interpretation of natural language by means of Equational First Order Logic (FOL). The communicative acts of the human agent are automatically converted into a First Order Logic theory. The models of the FOL theory are analysed by Mace4 model finder. To goal of the chatbot is to reduce the number of interpretation models. With a single interpretation model, the chatbot is sure about the human agent preferences. To reduce the number of models, the agent asks questions, while the corresponding answers are used to restrict the models. The order in which these questions are asked is extremely relevant to effectevely reduce the huge number of interpretation models of a FOL theory. We apply here an entropy-based method to improve the dialogues between a conversational agent and a human agen. The new method computes the entropy on the set of possible interpretation models. The experiments indicated that with entropy-based chatbots it is easier to estimate the budget of questions needed to elicit client preferences.