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Probabilistic Logic for Intelligent Systems

  • Thomas C. HendersonEmail author
  • Robert Simmons
  • Bernard Serbinowski
  • Xiuyi Fan
  • Amar Mitiche
  • Michael Cline
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 867)

Abstract

Given a knowledge base in Conjunctive Normal Form for use by an intelligent agent, with probabilities assigned to the conjuncts, the probability of any new query sentence can be determined by solving the Probabilistic Satisfiability Problem (PSAT). This involves finding a consistent probability distribution over the atoms (if they are independent) or complete conjunction set of the atoms. We show how this problem can be expressed and solved as a set of nonlinear equations derived from the knowledge base sentences and standard probability of logical sentences. Evidence is given that numerical gradient descent algorithms can be used more effectively then other current methods to find PSAT solutions.

Keywords

PSAT Probabilistic knowledge base Nonlinear systems 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Thomas C. Henderson
    • 1
  • Robert Simmons
    • 1
  • Bernard Serbinowski
    • 1
  • Xiuyi Fan
    • 2
  • Amar Mitiche
    • 3
  • Michael Cline
    • 4
  1. 1.University of UtahSalt Lake CityUSA
  2. 2.University of SwanseaSwanseaWales
  3. 3.University of MontrealMontrealCanada
  4. 4.Simon Fraser UnviersityBurnabyCanada

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