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@COMMENT This file came from Alan K. Mackworth's publication pages at
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@InProceedings{IJCAI05,
  author =	 {L. Chang and Alan K. Mackworth},
  title =	 {A Generalization of Generalized Arc Consistency: From Constraint Satisfaction to Constraint-Based Inference},
  year =	 {2005}, 
  month =        {July},
  booktitle =	 {Proc. IJCAI-05 Workshop on Modelling and Solving Problems with Constraints}, 
  address =      {Edinburgh, UK},
  pages =         {68--75},
  abstract =	 {Arc consistency and generalized arc consistency
                  are two of the most important local consistency
                  techniques for binary and non-binary classic constraint
                  satisfaction problems (CSPs). Based on
                  the Semiring CSP and Valued CSP frameworks,
                  arc consistency has also been extended to handle
                  soft constraint satisfaction problems such as fuzzy
                  CSP, probabilistic CSP, max CSP, and weighted
                  CSP. This extension is based on an idempotent or
                  strictly monotonic constraint combination operator.
                  In this paper, we present a weaker condition for
                  applying the generalized arc consistency approach
                  to constraint-based inference problems other than
                  classic and soft CSPs. These problems, including
                  probability inference and maximal likelihood decoding,
                  can be processed using generalized arc consistency
                  enforcing approaches. We also show that,
                  given an additional monotonic condition on the corresponding
                  semiring structure, some of constraintbased
                  inference problems can be approximately
                  preprocessed using generalized arc consistency algorithms.},
  bib2html_pubtype ={Refereed Conference Proceeding},
  bib2html_rescat ={},
}