Symbolic and Quantitative Approaches to Reasoning and Uncertainty

Volume 1638 of the series Lecture Notes in Computer Science pp 279-292


Probabilisitc Logic Programming under Maximum Entropy

  • Thomas LukasiewiczAffiliated withInstitut für Informatik, Universität Gießen
  • , Gabriele Kern-IsbernerAffiliated withFachbereich Informatik, FernUniversität Hagen

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In this paper, we focus on the combination of probabilistic logic programming with the principle of maximum entropy. We start by defining probabilistic queries to probabilistic logic programs and their answer substitutions under maximum entropy. We then present an efficient linear programming characterization for the problem of deciding whether a probabilistic logic program is satisfiable. Finally, and as a central contribution of this paper, we introduce an efficient technique for approximative probabilistic logic programming under maximum entropy. This technique reduces the original entropy maximization task to solving a modified and relatively small optimization problem.