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 m...
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Published in | Advances in Intelligent Computing pp. 228 - 237 |
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Main Authors | , |
Format | Book Chapter Conference Proceeding |
Language | English |
Published |
Berlin, Heidelberg
Springer Berlin Heidelberg
2005
Springer |
Series | Lecture Notes in Computer Science |
Subjects | |
Online Access | Get full text |
ISBN | 3540282270 9783540282273 3540282262 9783540282266 |
ISSN | 0302-9743 1611-3349 |
DOI | 10.1007/11538356_24 |
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Abstract | 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. |
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AbstractList | 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. |
Author | Lim, Sungsoo Cho, Sung-Bae |
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Editor | Huang, Guang-Bin Huang, De-Shuang Zhang, Xiao-Ping |
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Keywords | Conditional distribution Scalability Database query User interface Bayes network Intention Logic gate Knowledge base Internet Conditional probability Artificial intelligence Intelligent agent |
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SubjectTerms | Applied sciences Artificial intelligence Bayesian network Computer science; control theory; systems Conversational agent Exact sciences and technology Hierarchical structure Noisy-OR gate Script |
Title | Automatic Construction of Bayesian Networks for Conversational Agent |
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