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Probabilistic Spatial Reasoning in Constraint Logic Programming
University of Münster, Münster, Germany; The DesignSpace Group, Bremen, Germany.
University of Bremen, Bremen, Germany; The DesignSpace Group, Bremen, Germany. (AASS)ORCID iD: 0000-0002-6290-5492
University of Bremen, Bremen, Germany; The DesignSpace Group, Bremen, Germany.
2016 (English)In: Scalable Uncertainty Management: 10th International Conference, SUM 2016, Nice, France, September 21-23, 2016, Proceedings / [ed] Steven Schockaert, Pierre Senellart, Springer, 2016, p. 289-302Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we present a novel framework and full implementation of probabilistic spatial reasoning within a Logic Programming context. The crux of our approach is extending Probabilistic Logic Programming (based on distribution semantics) to support reasoning over spatial variables via Constraint Logic Programming. Spatial reasoning is formulated as a numerical optimisation problem, and we implement our approach within ProbLog 1. We demonstrate a range of powerful features beyond what is currently provided by existing probabilistic and spatial reasoning tools.

Place, publisher, year, edition, pages
Springer, 2016. p. 289-302
Series
Lecture Notes in Artificial Intelligence, ISSN 0302-9743, E-ISSN 1611-3349 ; 9858
Keyword [en]
Probabilistic Logic Programming; Constraint Logic Programming; Declarative spatial reasoning
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-63601DOI: 10.1007/978-3-319-45856-4_20ISI: 000388435800020Scopus ID: 2-s2.0-84988030183ISBN: 978-3-319-45855-7 (print)ISBN: 978-3-319-45856-4 (print)OAI: oai:DiVA.org:oru-63601DiVA, id: diva2:1168910
Conference
10th International Conference (SUM 2016), Nice, France, September 21-23, 2016
Available from: 2017-12-21 Created: 2017-12-21 Last updated: 2018-01-19Bibliographically approved

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Bhatt, Mehul

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Output format
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