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Logic Programming and Uncertainty

  • Chitta Baral
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6929)

Abstract

In recent years Logic programming based languages and features–such as rules and non-monotonic constructs–have become important in various knowledge representation paradigms. While the early logic programming languages, such as Horn logic programs and Prolog did not focus on expressing and reasoning with uncertainty, in recent years logic programming languages have been developed that can express both logical and quantitative uncertainty. In this paper we give an overview of such languages and the kind of uncertainty they can express and reason with. Among those, we slightly elaborate on the language P-log that not only accommodates probabilistic reasoning, but also respects causality and distinguishes observational and action updates.

Keywords

Logic Program Logic Programming Stable Model Conditional Probability Table Logic Programming Language 
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 Berlin Heidelberg 2011

Authors and Affiliations

  • Chitta Baral
    • 1
  1. 1.Faculty of Computer Science and EngineeringArizona State UniversityTempe

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