Conservative and reward-driven behavior selection in a commonsense reasoning framework

Johnston, B; Williams, MA

Conservative and reward-driven behavior selection in a commonsense reasoning framework

Johnston, B Williams, MA

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Publication Type:: Conference Proceeding
Citation:: AAAI Fall Symposium - Technical Report, 2009, FS-09-05 pp. 14 - 19
Issue Date:: 2009-12-01

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Field	Value	Language
dc.contributor.author	Johnston, B	en_US
dc.contributor.author	Williams, MA https://orcid.org/0000-0002-1047-0503	en_US
dc.date.issued	2009-12-01	en_US
dc.identifier.citation	AAAI Fall Symposium - Technical Report, 2009, FS-09-05 pp. 14 - 19	en_US
dc.identifier.isbn	9781577354390	en_US
dc.identifier.uri	http://hdl.handle.net/10453/12715
dc.description.abstract	Comirit is a framework for commonsense reasoning that combines simulation, logical deduction and passive machine learning. While a passive, observation-driven approach to learning is safe and highly conservative, it is limited to interaction only with those objects that it has previously observed. In this paper we describe a preliminary exploration of methods for extending Comirit to allow safe action selection in uncertain situations, and to allow reward-maximizing selection of behaviors. Copyright © 2009, Association for the Advancement of Artificial Intelligence. All rights reserved.	en_US
dc.relation.ispartof	AAAI Fall Symposium - Technical Report	en_US
dc.title	Conservative and reward-driven behavior selection in a commonsense reasoning framework	en_US
dc.type	Conference Proceeding
utslib.citation.volume	FS-09-05	en_US
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	150307 Innovation and Technology Management	en_US
utslib.for	150302 Business Information Systems	en_US
dc.location.activity	Washington, USA	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/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	FS-09-05	en_US

Abstract:

Comirit is a framework for commonsense reasoning that combines simulation, logical deduction and passive machine learning. While a passive, observation-driven approach to learning is safe and highly conservative, it is limited to interaction only with those objects that it has previously observed. In this paper we describe a preliminary exploration of methods for extending Comirit to allow safe action selection in uncertain situations, and to allow reward-maximizing selection of behaviors. Copyright © 2009, Association for the Advancement of Artificial Intelligence. All rights reserved.

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http://hdl.handle.net/10453/12715