Encoding large RCC8 scenarios using rectangular pseudo-solutions

Long, Z; Schockaert, S; Li, S

Encoding large RCC8 scenarios using rectangular pseudo-solutions

Long, Z

Schockaert, S Li, S

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Publication Type:: Conference Proceeding
Citation:: Proc. Int. Workshop Tempor. Represent. Reason., 2016, pp. 463 - 472
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Long, Z https://orcid.org/0000-0002-2714-3453	en_US
dc.contributor.author	Schockaert, S	en_US
dc.contributor.author	Li, S https://orcid.org/0000-0002-3332-2546	en_US
dc.date.issued	2016-01-01	en_US
dc.identifier.citation	Proc. Int. Workshop Tempor. Represent. Reason., 2016, pp. 463 - 472	en_US
dc.identifier.uri	http://hdl.handle.net/10453/98211
dc.description.abstract	Copyright © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Most approaches in the field of qualitative spatial reasoning (QSR) use constraint networks to encode spatial scenarios. The size of these networks is quadratic in the number of variables, which has severely limited the real-world application of QSR. In this paper, we propose another representation of spatial scenarios, in which each variable is associated with one or more rectangles. Instead of requiring these rectangles to define a solution of the corresponding constraint network, we construct sequences of rectangles that define partial solutions to progressively weaker constraint networks. We present experimental results that illustrate the effectiveness of this strategy.	en_US
dc.relation.ispartof	Proc. Int. Workshop Tempor. Represent. Reason.	en_US
dc.title	Encoding large RCC8 scenarios using rectangular pseudo-solutions	en_US
dc.type	Conference Proceeding
utslib.for	170203 Knowledge Representation and Machine Learning	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Copyright © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Most approaches in the field of qualitative spatial reasoning (QSR) use constraint networks to encode spatial scenarios. The size of these networks is quadratic in the number of variables, which has severely limited the real-world application of QSR. In this paper, we propose another representation of spatial scenarios, in which each variable is associated with one or more rectangles. Instead of requiring these rectangles to define a solution of the corresponding constraint network, we construct sequences of rectangles that define partial solutions to progressively weaker constraint networks. We present experimental results that illustrate the effectiveness of this strategy.

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http://hdl.handle.net/10453/98211