Fusion of natural language propositions: Bayesian random set framework

Bishop, AN; Ristic, B

Fusion of natural language propositions: Bayesian random set framework

Bishop, AN

Ristic, B

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Publication Type:: Conference Proceeding
Citation:: Fusion 2011 - 14th International Conference on Information Fusion, 2011
Issue Date:: 2011-09-13

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Field	Value	Language
dc.contributor.author	Bishop, AN https://orcid.org/0000-0001-8581-8562	en_US
dc.contributor.author	Ristic, B	en_US
dc.date.issued	2011-09-13	en_US
dc.identifier.citation	Fusion 2011 - 14th International Conference on Information Fusion, 2011	en_US
dc.identifier.isbn	9781457702679	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119665
dc.description.abstract	This work concerns an automatic information fusion scheme for state estimation where the inputs (or measurements) that are used to reduce the uncertainty in the state of a subject are in the form of natural language propositions. In particular, we consider spatially referring expressions concerning the spatial location (or state value) of certain subjects of interest with respect to known anchors in a given state space. The probabilistic framework of random-set-based estimation is used as the underlying mathematical formalism for this work. Each statement is used to generate a generalized likelihood function over the state space. A recursive Bayesian filter is outlined that takes, as input, a sequence of generalized likelihood functions generated by multiple statements. The idea is then to recursively build a map, e.g. a posterior density map, over the state space that can be used to infer the subject state. © 2011 IEEE.	en_US
dc.relation.ispartof	Fusion 2011 - 14th International Conference on Information Fusion	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Fusion of natural language propositions: Bayesian random set framework	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic Engineering	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/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

This work concerns an automatic information fusion scheme for state estimation where the inputs (or measurements) that are used to reduce the uncertainty in the state of a subject are in the form of natural language propositions. In particular, we consider spatially referring expressions concerning the spatial location (or state value) of certain subjects of interest with respect to known anchors in a given state space. The probabilistic framework of random-set-based estimation is used as the underlying mathematical formalism for this work. Each statement is used to generate a generalized likelihood function over the state space. A recursive Bayesian filter is outlined that takes, as input, a sequence of generalized likelihood functions generated by multiple statements. The idea is then to recursively build a map, e.g. a posterior density map, over the state space that can be used to infer the subject state. © 2011 IEEE.

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http://hdl.handle.net/10453/119665