A computational architecture for conversation
UM '99 Proceedings of the seventh international conference on User modeling
ELIZA—a computer program for the study of natural language communication between man and machine
Communications of the ACM
Toward conversational human-computer interaction
AI Magazine
Data Mining and Knowledge Discovery
Parallel Learning of Belief Networks in Large and Difficult Domains
Data Mining and Knowledge Discovery
Scalable Techniques for Mining Causal Structures
Data Mining and Knowledge Discovery
IEEE Intelligent Systems
Natural Language Sales Assistant - A Web-Based Dialog System for Online Sales
Proceedings of the Thirteenth Conference on Innovative Applications of Artificial Intelligence Conference
An Effective Conversational Agent with User Modeling Based on Bayesian Network
WI '01 Proceedings of the First Asia-Pacific Conference on Web Intelligence: Research and Development
Conversation as action under uncertainty
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
The lumière project: Bayesian user modeling for inferring the goals and needs of software users
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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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.