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	<event:Event rdf:about="http://ebiquity.umbc.edu/event/html/id/140/Preference-Elicitation-in-Constraint-Based-Decision-Problems">
		<rdfs:label><![CDATA[Preference Elicitation in Constraint-Based Decision Problems]]></rdfs:label>
		<event:title><![CDATA[Preference Elicitation in Constraint-Based Decision Problems]]></event:title>
		<event:speaker>
<person:Collaborator rdf:about="http://ebiquity.umbc.edu/person/html/Craig/Boutilier"><person:name><![CDATA[Craig Boutilier]]></person:name><rdfs:label><![CDATA[Craig Boutilier]]></rdfs:label></person:Collaborator>
		</event:speaker>
		<event:startDate rdf:datatype="&xsd;dateTime">2006-04-07T13:00:00-05:00</event:startDate>
		<event:endDate rdf:datatype="&xsd;dateTime">2006-04-07T14:00:00-05:00</event:endDate>
		<event:location><![CDATA[325b]]></event:location>
		<event:abstract><![CDATA[Preference elicitation is generally required when making or recommending
decisions on behalf of users whose utility function is not known with
certainty. Although one can engage in elicitation until a utility
function is perfectly known, in practice, this is infeasible. This talk
tackles this problem in constraint-based optimization. I will first
describe a graphical model for utility representation and issues
associated with elicitation in this model. I then discuss two methods
for optimization with imprecise utility information: a Bayesian approach
in which utility function  ncertainty is quantified probabilistically;
and a distribution-free minimax
regret model. Finally, I will describe several heuristic strategies for
elicitation.
<p>
This work describes several joint projects with: Darius Braziunas, Relu
Patrascu, Pascal Poupart and Dale Schuurmans.
]]></event:abstract>
		<event:host>
<person:Collaborator rdf:about="http://ebiquity.umbc.edu/person/html/Marie/desJardins"><person:name><![CDATA[Marie desJardins]]></person:name><rdfs:label><![CDATA[Marie desJardins]]></rdfs:label></person:Collaborator>
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