Artificial Intelligence
Quantitative deduction and its fixpoint theory
Journal of Logic Programming
Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
A logic for reasoning about probabilities
Information and Computation - Selections from 1988 IEEE symposium on logic in computer science
An analysis of first-order logics of probability
Artificial Intelligence
On selecting a satisfying truth assignment (extended abstract)
SFCS '91 Proceedings of the 32nd annual symposium on Foundations of computer science
Probabilistic logic programming
Information and Computation
Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
ProbView: a flexible probabilistic database system
ACM Transactions on Database Systems (TODS)
Heterogeneous active agents, I: semantics
Artificial Intelligence
A Parametric Approach to Deductive Databases with Uncertainty
IEEE Transactions on Knowledge and Data Engineering
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Column Generation Methods for Probabilistic Logic
Proceedings of the 1st Integer Programming and Combinatorial Optimization Conference
Probabilistic Logic Programming and Bayesian Networks
ACSC '95 Proceedings of the 1995 Asian Computing Science Conference on Algorithms, Concurrency and Knowledge
Coherent Well-founded Annotated Logic Programs
LPNMR '99 Proceedings of the 5th International Conference on Logic Programming and Nonmonotonic Reasoning
Probalilistic Logic Programming under Maximum Entropy
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
A Probabilistic Algorithm for k-SAT and Constraint Satisfaction Problems
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Optimal Models of Disjunctive Logic Programs: Semantics, Complexity, and Computation
IEEE Transactions on Knowledge and Data Engineering
Optimal status sets of heterogeneous agent programs
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
A stochastic language for modelling opponent agents
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Mathematical aspects of mixing times in Markov chains
Foundations and Trends® in Theoretical Computer Science
Annals of Mathematics and Artificial Intelligence
Finding Most Probable Worlds of Probabilistic Logic Programs
SUM '07 Proceedings of the 1st international conference on Scalable Uncertainty Management
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
COBA 2.0: A Consistency-Based Belief Change System
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Logic programs with uncertainties: a tool for implementing rule-based systems
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
A new method for solving hard satisfiability problems
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
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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 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.