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        <identifier>oai:drops-oai.dagstuhl.de:7923</identifier>
        <datestamp>2024-03-06T10:41:36Z</datestamp>
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          <dc:title>Collective Singleton-Based Consistency for Qualitative Constraint Networks</dc:title>
          <dc:creator>Sioutis, Michael</dc:creator>
          <dc:creator>Paparrizou, Anastasia</dc:creator>
          <dc:creator>Condotta, Jean-François</dc:creator>
          <dc:subject>Qualitative constraint network</dc:subject>
          <dc:subject>qualitative spatial and temporal reasoning</dc:subject>
          <dc:subject>partial singleton path-consistency</dc:subject>
          <dc:subject>local consistency</dc:subject>
          <dc:subject>minimal labeling pr</dc:subject>
          <dc:description>Partial singleton closure under weak composition, or partial singleton (weak) path-consistency for short, is essential for approximating satisfiability of qualitative constraints networks. Briefly put, partial singleton path-consistency ensures that each base relation of each of the constraints of a qualitative constraint network can define a singleton relation in the corresponding partial closure of that network under weak composition, or in its corresponding partially (weak) path-consistent subnetwork for short. In particular, partial singleton path-consistency has been shown to play a crucial role in tackling the minimal labeling problem of a qualitative constraint network, which is the problem of finding the strongest implied constraints of that network. In this paper, we propose a stronger local consistency that couples partial singleton path-consistency with the idea of collectively deleting certain unfeasible base relations by exploiting singleton checks. We then propose an efficient algorithm for enforcing this consistency that, given a qualitative constraint network, performs fewer constraint checks than the respective algorithm for enforcing partial singleton path-consistency in that network. We formally prove certain properties of our new local consistency, and motivate its usefulness through demonstrative examples and a preliminary experimental evaluation with qualitative constraint networks of Interval Algebra.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Michael Sioutis and Anastasia Paparrizou and Jean-François Condotta</dc:contributor>
          <dc:date>2017</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 90, 24th International Symposium on Temporal Representation and Reasoning (TIME 2017)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.TIME.2017.19</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-79237</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2017.19</dc:identifier>
          <dc:language>eng</dc:language>
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