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Markov Logic as a Modelling Language for Weighted Constraint Satisfaction Problems (bibtex)
  author = {Jain, Dominik and Maier, Paul and Wylezich, Gregor},
  title = {Markov Logic as a Modelling Language for Weighted Constraint Satisfaction
  booktitle = {Eighth International Workshop on Constraint Modelling and Reformulation,
	in conjunction with {CP2009}},
  year = {2009},
  abstract = {Many real-world problems, for example resource allocation, can be
	formalized as soft constraint optimization problems. A fundamental
	issue is the compact and precise declaration of such problems. We
	propose Markov logic networks ({MLNs)}, a representation formalism
	well-known from statistical relational learning, as a simple yet
	highly expressive modelling framework, for {MLNs} enable the representation
	of general principles that abstract away from concrete entities in
	order to achieve a separation between the model and the data to which
	it is applied. {MLNs} provide the full power of first-order logic
	and combine it with probabilistic semantics, thus allowing a flexible
	representation of soft constraints. We introduce an automatic conversion
	of maximum a posteriori ({MAP)} inference problems in {MLNs} to weighted
	constraint satisfaction problems to leverage a large body of available
	solving methods, and we make our software suite available to the
	public. We demonstrate the soundness of our approach on a real-world
	room allocation problem, providing experimental results.}
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Last edited 29.01.2013 17:37 by Quirin Lohr