
Computing LPMLN Using ASP and MLN Solvers
LPMLN is a recent addition to probabilistic logic programming languages....
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A Minesweeper Solver Using Logic Inference, CSP and Sampling
Minesweeper as a puzzle video game and is proved that it is an NPC probl...
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Adaptive MCMCBased Inference in Probabilistic Logic Programs
Probabilistic Logic Programming (PLP) languages enable programmers to sp...
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Stochastic Logic Programs: Sampling, Inference and Applications
Algorithms for exact and approximate inference in stochastic logic progr...
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ConstraintBased Inference in Probabilistic Logic Programs
Probabilistic Logic Programs (PLPs) generalize traditional logic program...
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Quantum Enhanced Inference in Markov Logic Networks
Markov logic networks (MLNs) reconcile two opposing schools in machine l...
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Probabilistic Semantics and Defaults
There is much interest in providing probabilistic semantics for defaults...
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PASOCS: A Parallel Approximate Solver for Probabilistic Logic Programs under the Credal Semantics
The Credal semantics is a probabilistic extension of the answer set semantics which can be applied to programs that may or may not be stratified. It assigns to atoms a set of acceptable probability distributions characterised by its lower and upper bounds. Performing exact probabilistic inference in the Credal semantics is computationally intractable. This paper presents a first solver, based on sampling, for probabilistic inference under the Credal semantics called PASOCS (Parallel Approximate SOlver for the Credal Semantics). PASOCS performs both exact and approximate inference for queries given evidence. Approximate solutions can be generated using any of the following sampling methods: naive sampling, MetropolisHastings and Gibbs Markov Chain MonteCarlo. We evaluate the fidelity and performance of our system when applied to both stratified and nonstratified programs. We perform a sanity check by comparing PASOCS to available systems for stratified programs, where the semantics agree, and show that our system is competitive on unstratified programs.
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