Scaling Most Probable World Computations in Probabilistic Logic Programs

Authors:
Gerardo I. Simari;Maria Vanina Martinez;Amy Sliva;V. S. Subrahmanian
Affiliations:
University of Maryland College Park, USA MD 20742;University of Maryland College Park, USA MD 20742;University of Maryland College Park, USA MD 20742;University of Maryland College Park, USA MD 20742
Venue:
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
Year:
2008

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Abstract

The "Most Probable World" (MPW) problem in probabilistic logic programming (PLPs) is that of finding a possible world with the highest probability. Past work has shown that this problem is computationally intractable and involves solving exponentially many linear programs, each of which is of exponential size. In this paper, we study what happens when the user focuses his interest on a set of atoms in such a PLP. We show that we can significantly reduce the number of worlds to be considered by defining a "reduced" linear program whose solution is in one-one correspondence with the exact solution to the MPW problem. However, the problem is still intractable. We develop a Monte Carlo sampling approach that enables us to build a quick approximation of the reduced linear program that allows us to estimate (inexactly) the exact solution to the MPW problem. We show experimentally that our approach works well in practice, scaling well to problems where the exact solution is intractable to compute.