Continuous learning as a service for conversational virtual agents

Abstract

IT support services are moving towards self assist mode by means of cognitive agents. Such cognitive agents are typically being designed as conversational system. It is important that as the agent interacts with users, it should continuously observe, infer and learn as to what is it that it is doing well, what topics is it not able to handle well and what topics it does not seem to know about at all. In this paper, we have proposed a service that enables feedback based learning in cognitive agents. Conversation systems typically support feedback mechanism for example, some of them may ask the users to vote for the answers, or rate the experience/response that they got for their query. We propose a reinforcement learning based model for the agent to continuously learn and improve. To the best of our knowledge, this is a first attempt in modeling the continuous learning problem in conversational systems as a reinforcement learning problem. We also provide the service design for continuous learning as a service in context of conversational agents. We have evaluated the model against real data to show how the learning is helpful in improving agent’s performance. The model can also be generalized for any supervised classification problem.