Automatic construction of Bayesian networks for conversational agent

As the information in the internet proliferates, the methods for effectively providing the information have been exploited, especially in conversational agents. Bayesian network is applied to infer the intention of user's query. Since the construction of Bayesian network requires large efforts and much time, an automatic method for it might be useful for applying conversational agents to several applications. In order to improve the scalability of the agent, in this paper, we propose a method of automatically generating Bayesian networks from scripts composing knowledge base of the conversational agent. It constructs the structure of hierarchically composing nodes and learns the conditional probability distribution table using Noisy-OR gate. The experimental results with subjects confirm the usefulness of the proposed method.