@InProceedings{jain09modref,
  author =       {Dominik Jain and Paul Maier and Gregor Wylezich},
  title =        {{Markov Logic as a Modelling Language for Weighted Constraint Satisfaction Problems}},
  booktitle =    {Eighth International Workshop on Constraint Modelling and Reformulation, in conjunction with CP2009},
  year =         {2009},
  bib2html_pubtype = {Conference Paper},
  bib2html_rescat  = {Markov Logic, WCSP},
  bib2html_groups =  {ProbCog},
  bib2html_funding = {CoTeSys},
  bib2html_domain  = {},
  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.}
}