Robot reasoning using first order bayesian networks

Raza, S; Haider, S; Williams, MA

Robot reasoning using first order bayesian networks

Raza, S Haider, S Williams, MA

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 8032 LNAI pp. 1 - 12
Issue Date:: 2013-07-17

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Field	Value	Language
dc.contributor.author	Raza, S	en_US
dc.contributor.author	Haider, S	en_US
dc.contributor.author	Williams, MA https://orcid.org/0000-0002-1047-0503	en_US
dc.date.issued	2013-07-17	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 8032 LNAI pp. 1 - 12	en_US
dc.identifier.isbn	9783642395147	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26581
dc.description.abstract	This study presents the application of first-order Bayesian Networks (FOBN) to model and reason in domains with complex relational and rich probabilistic structures. The FOBN framework used in this study is 'multi-entity Bayesian networks (MEBN). MEBN has its roots in Bayesian networks and aims to overcome some key modeling limitations of Bayesian networks by supplementing them with the expressive power of first-order logic. The study has been conducted in the domain of RoboCup Soccer which provides a challenging benchmark platform to evaluate the applicability of any knowledge representation mechanism. The benchmark scenario in this paper comprises of a soccer playing agent who is in possession of the ball and needs to decide the best action to be performed in a specific game situation. Further intricacies of this scenario have been discussed to thoroughly assess the effectiveness of first-order Bayesian network in the domain of RoboCup Soccer and it is found to provide the essential expressive power required to facilitate decision-making in such complex, stochastic domains. © 2013 Springer-Verlag Berlin Heidelberg.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-39515-4-1	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Robot reasoning using first order bayesian networks	en_US
dc.type	Conference Proceeding
utslib.citation.volume	8032 LNAI	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/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	8032 LNAI	en_US

Abstract:

This study presents the application of first-order Bayesian Networks (FOBN) to model and reason in domains with complex relational and rich probabilistic structures. The FOBN framework used in this study is 'multi-entity Bayesian networks (MEBN). MEBN has its roots in Bayesian networks and aims to overcome some key modeling limitations of Bayesian networks by supplementing them with the expressive power of first-order logic. The study has been conducted in the domain of RoboCup Soccer which provides a challenging benchmark platform to evaluate the applicability of any knowledge representation mechanism. The benchmark scenario in this paper comprises of a soccer playing agent who is in possession of the ball and needs to decide the best action to be performed in a specific game situation. Further intricacies of this scenario have been discussed to thoroughly assess the effectiveness of first-order Bayesian network in the domain of RoboCup Soccer and it is found to provide the essential expressive power required to facilitate decision-making in such complex, stochastic domains. © 2013 Springer-Verlag Berlin Heidelberg.

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http://hdl.handle.net/10453/26581