Automatic Construction of Hierarchical Bayesian Networks for Topic Inference of Conversational Agent
Sung-Soo Lim, Sung‐Bae Cho · Jeongbo gwahaghoe nonmunji. so'peuteuweeo mich eung'yong · 2006
Recently it is proposed that the Bayesian networks used as conversational agent for topic inference is useful but the Bayesian networks require much time to model, and the Bayesian networks also have to be modified when the scripts, the database for conversation, are added or modified and this hinders the scalability of the agent. This paper presents a method to improve the scalability of the agent by constructing the Bayesian network from scripts automatically. The proposed method is to model the structure of Bayesian networks hierarchically and to utilize Noisy-OR gate to form the conditional probability distribution table (CPT). Experimental results with ten subjects confirm the usefulness of the proposed method.