A representation theorem for minmax regret policies

Li, S

A representation theorem for minmax regret policies

Li, S

Permalink

Publication Type:: Journal Article
Citation:: Artificial Intelligence, 2007, 171 (1), pp. 19 - 24
Issue Date:: 2007-01-01

Closed Access

	Filename	Description	Size
	2007004952OK.pdf		182.11 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Li, S https://orcid.org/0000-0002-3332-2546	en_US
dc.date.issued	2007-01-01	en_US
dc.identifier.citation	Artificial Intelligence, 2007, 171 (1), pp. 19 - 24	en_US
dc.identifier.issn	0004-3702	en_US
dc.identifier.uri	http://hdl.handle.net/10453/8958
dc.description.abstract	Decision making under uncertainty is one of the central tasks of artificial agents. Due to their simplicity and ease of specification, qualitative decision tools are popular in artificial intelligence. Brafman and Tennenholtz [R.I. Brafman, M. Tennenholtz, An axiomatic treatment of three qualitative decision criteria, J. ACM 47 (3) (2000) 452-482] model an agent's uncertain knowledge as her local state, which consists of states of the world that she deems possible. A policy determines for each local state a total preorder of the set of actions, which represents the agent's preference over these actions. It is known that a policy is maximin representable if and only if it is closed under unions and satisfies a certain acyclicity condition. In this paper we show that the above conditions, although necessary, are insufficient for minmax regret and competitive ratio policies. A complete characterization of these policies is obtained by introducing the best-equally strictness. © 2006 Elsevier B.V. All rights reserved.	en_US
dc.relation.ispartof	Artificial Intelligence	en_US
dc.relation.isbasedon	10.1016/j.artint.2006.11.001	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A representation theorem for minmax regret policies	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	171	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
utslib.for	1702 Cognitive Sciences	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
utslib.copyright.status	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	171	en_US

Abstract:

Decision making under uncertainty is one of the central tasks of artificial agents. Due to their simplicity and ease of specification, qualitative decision tools are popular in artificial intelligence. Brafman and Tennenholtz [R.I. Brafman, M. Tennenholtz, An axiomatic treatment of three qualitative decision criteria, J. ACM 47 (3) (2000) 452-482] model an agent's uncertain knowledge as her local state, which consists of states of the world that she deems possible. A policy determines for each local state a total preorder of the set of actions, which represents the agent's preference over these actions. It is known that a policy is maximin representable if and only if it is closed under unions and satisfies a certain acyclicity condition. In this paper we show that the above conditions, although necessary, are insufficient for minmax regret and competitive ratio policies. A complete characterization of these policies is obtained by introducing the best-equally strictness. © 2006 Elsevier B.V. All rights reserved.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/8958