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Jaime G. Carbonell

2013

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Scheduling With Uncertain Resources: Learning To Ask The Right Questions, Alexander Carpentier, Mehrbod Sharifi, Eugene Fink, Jaime G. Carbonell May 2013

Scheduling With Uncertain Resources: Learning To Ask The Right Questions, Alexander Carpentier, Mehrbod Sharifi, Eugene Fink, Jaime G. Carbonell

Jaime G. Carbonell

We consider the task of scheduling a conference based on incomplete information about resources and constraints, which requires elicitation of additional data, and describe a learning procedure that improves elicitation strategies. We outline the representation of incomplete knowledge, and then describe an adaptive elicitation procedure, which learns to identify critical missing data.


Scheduling With Uncertain Resources: Learning To Make Reasonable Assumptions, Steven Gardiner, Eugene Fink, Jaime G. Carbonell May 2013

Scheduling With Uncertain Resources: Learning To Make Reasonable Assumptions, Steven Gardiner, Eugene Fink, Jaime G. Carbonell

Jaime G. Carbonell

We consider the task of scheduling a conference based on incomplete information about resources and constraints, and describe a mechanism for the dynamic learning of related default assumptions, which enable the scheduling system to make reasonable guesses about missing data. We outline the representation of incomplete knowledge, describe the learning procedure, and demonstrate that the learned knowledge improves the scheduling results.


Scheduling With Uncertain Resources: Representation Of Common Knowledge, Eugene Fink, P. Matthew Jennings, Konstantin Salomatin, Jaime G. Carbonell May 2013

Scheduling With Uncertain Resources: Representation Of Common Knowledge, Eugene Fink, P. Matthew Jennings, Konstantin Salomatin, Jaime G. Carbonell

Jaime G. Carbonell

We describe a system for scheduling a conference based on incomplete information about available resources and scheduling constraints. We explain the representation of uncertain knowledge and related common-sense rules, which allow reasoning based on uncertain and partially missing data.