Reasoning with Uncertainty

  • Wolfgang Ertel
Part of the Undergraduate Topics in Computer Science book series (UTICS)


Reasoning under uncertainty with limited resources and incomplete knowledge plays a big role in everyday situations and also in many technical applications of AI. Probabilistic reasoning is the modern AI method for solving these problems. After a brief introduction to probability theory we present the powerful method of maximum entropy and Bayesian networks which are used in many applications. The medical diagnosis expert system LEXMED, developed by the author, is used to demonstrate the power of these formalisms.


Conditional Probability Expert System Bayesian Network Parent Node Conditional Independence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.FB Elektrotechnik und InformatikHochschule Ravensburg-Weingarten, University of Applied SciencesWeingartenGermany

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