This paper describes an extension to the constraint satisfaction problem
(CSP) called MUSE CSP (MUltiply SEgmented Constraint Satisfaction Problem).
This extension is especially useful for those problems which segment into
multiple sets of partially shared variables. Such problems arise naturally in
signal processing applications including computer vision, speech processing,
and handwriting recognition. For these applications, it is often difficult to
segment the data in only one way given the low-level information utilized by
the segmentation algorithms. MUSE CSP can be used to compactly represent
several similar instances of the constraint satisfaction problem. If multiple
instances of a CSP have some common variables which have the same domains and
constraints, then they can be combined into a single instance of a MUSE CSP,
reducing the work required to apply the constraints. We introduce the concepts
of MUSE node consistency, MUSE arc consistency, and MUSE path consistency. We
then demonstrate how MUSE CSP can be used to compactly represent lexically
ambiguous sentences and the multiple sentence hypotheses that are often
generated by speech recognition algorithms so that grammar constraints can be
used to provide parses for all syntactically correct sentences. Algorithms for
MUSE arc and path consistency are provided. Finally, we discuss how to create a
MUSE CSP from a set of CSPs which are labeled to indicate when the same
variable is shared by more than a single CSP.Comment: See http://www.jair.org/ for any accompanying file
