Dissertation
Dissertation > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory > Artificial Neural Networks and Computing

Study on Logical Argumentation and Bayesian Network Combination

Author HuXiaoFeng
Tutor XingYongKang
School Chongqing University
Course Computer Software and Theory
Keywords logical argumentation Bayesian network explanation mechanic effect-cause graph breast cancer recurrence
CLC TP183
Type Master's thesis
Year 2007
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Bayesian network model have been widely used in expert system of artificial intelligence, which combine logical causality with probabilistic computation to reduce the complexity of probability reasoning. On the one hand, Bayesian network model ensures the feasibility of inference. On the other hand, it also ensures a reliable explain for every result which deduced from Bayesian network. Bayesian network model create a classical and successful exemplification about combination of logic and probability. In spite of the reliability of Bayesian network model on probabilistic deduction, how could we find a reasonable and logical explanation for the inferential result? According to the Pearl who was the founder of Bayesian network, the network structure indicates a kind of logical causality, so the reasoning process is an argumentation process to the result. In the real application, the fact is not so, the network structure was built by experts who have in charge of professional knowledge on some certain area, usually, the initial network was not a suitable network satisfied with the presupposition of Bayesian model, but a casual-effect graph. The initial graph denotes logical and real cause-effect relationships which express human expert’s professional knowledge. Because that the effect nodes in cause-effect graph have more than two cause nodes commonly, it will take no effective influence on Bayesian reasoning. So in order to utilize the well-rounded inbeing Bayesian network reasoning technology, the cause-effect must be transformed. Although the result of transformation may cause a satisfied Bayesian network, but at the same time another problem come about, that is the initial cause-effect graph must be changed, and the Bayesian network after transformation would not denote a complete and logical cause and effect relationship expressed the professional knowledge of human experts. Half-baked information will attack the assumption on Pearl’s about one reasoning process is an argumentation process. If not take Bayesian network as argument on the explanation of inferential result, but obtain complete arguments from cause-effect graph, and find suitable arguments for the result. The process about finding causes for the being result just like searching arguments for or against the result, this kind of thinking model is similar with the logical argumentation system. So the main goal is combine the argumentation model which belongs to logic area and the Bayesian network model which belongs to probability area, to do so, it would satisfied not only utilizing mature Bayesian network reasoning to compute a precise probability value ,but also having a good and reliable explanation for the value.

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