Identifying latent beliefs in customer complaints to trigger epistemic rules for relevant human-bot dialog

During dialog with a customer for addressing his/her complaint the chatbot may pose questions or observations based on its underlying model. Sometimes the questions or observations posed may not be relevant given the nature of complaint and the current set of beliefs that the customer holds. We propose a model that uses machine-learning mechanism to categorize the complaint in order to identify the beliefs held by the customer and trigger epistemic rules in the domain. These rules help tailor the dialog and make it consistent with the set of beliefs of the customer. Such a model helps carry out meaningful conversations.