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On Approximate Solutions of Qualitative Constraint Networks

Li, Jason; Li, Sanjiang

Description

Qualitative Spatial and Temporal Reasoning (QSTR) represents spatial and temporal information in terms of human comprehensible qualitative predicates and reasons about qualitative information by solving qualitative constraint networks (QCNs). Despite significant progress in the past three decades, more and more evidence has shown that it is inherently hard to find exact solutions for expressive qualitative constraints. In many applications, however, we are often required to make decisions in a...[Show more]

dc.contributor.authorLi, Jason
dc.contributor.authorLi, Sanjiang
dc.coverage.spatialWashington United States of America
dc.date.accessioned2015-12-07T22:16:31Z
dc.date.createdNovember 4-6 2013
dc.identifier.isbn9781479929719
dc.identifier.urihttp://hdl.handle.net/1885/18067
dc.description.abstractQualitative Spatial and Temporal Reasoning (QSTR) represents spatial and temporal information in terms of human comprehensible qualitative predicates and reasons about qualitative information by solving qualitative constraint networks (QCNs). Despite significant progress in the past three decades, more and more evidence has shown that it is inherently hard to find exact solutions for expressive qualitative constraints. In many applications, however, we are often required to make decisions in a very limited time. In these cases, finding a good approximate solution in seconds is much more desirable than waiting days for an exact solution. In this paper, we will exploit the algebraic structure of qualitative calculi (e.g. Interval Algebra and RCC8) as well as their conceptual neighbourhood graphs to develop approximate methods for consistency checking in QSTR. Moreover, we propose and empirically compare four independent methods to serve as tools for finding good approximate solutions for the given qualitative calculi.
dc.publisherIEEE
dc.relation.ispartofseries25th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2013
dc.sourceProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
dc.titleOn Approximate Solutions of Qualitative Constraint Networks
dc.typeConference paper
local.description.notesImported from ARIES
local.description.refereedYes
dc.date.issued2013
local.identifier.absfor170203 - Knowledge Representation and Machine Learning
local.identifier.absfor080105 - Expert Systems
local.identifier.ariespublicationu4381505xPUB3
local.type.statusPublished Version
local.contributor.affiliationLi, Jason, College of Engineering and Computer Science, ANU
local.contributor.affiliationLi, Sanjiang, Tsinghua University
local.description.embargo2037-12-31
local.bibliographicCitation.startpage30
local.bibliographicCitation.lastpage37
local.identifier.doi10.1109/ICTAI.2013.16
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciences
dc.date.updated2015-12-07T07:53:17Z
local.identifier.scopusID2-s2.0-84897695581
CollectionsANU Research Publications

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