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Probabilistic Inductive Logic Programming Based on Answer Set Programming
[article]
2014
arXiv
pre-print
We propose a new formal language for the expressive representation of probabilistic knowledge based on Answer Set Programming (ASP). It allows for the annotation of first-order formulas as well as ASP rules and facts with probabilities and for learning of such weights from data (parameter estimation). Weighted formulas are given a semantics in terms of soft and hard constraints which determine a probability distribution over answer sets. In contrast to related approaches, we approach inference
arXiv:1405.0720v1
fatcat:ahr4hjvqmzf6hdy5cj34gsfesa