Scheduling with uncertain resources: Learning to ask the right questions

Abstract

Abstract-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.

Learning of personalized security settings

“…For example, a travel assistant may elicit user preferences [13]. We have applied similar techniques to eliciting preferences in the context of scheduling [2,3,6,7]. Researchers have also studied user modeling in recommender systems [10,14].…”

Scheduling with uncertain resources: Learning to make reasonable assumptions

“…The task of scheduling under uncertainty gives rise to several problems, including the representation of uncertain data, the automated construction of schedules based on these data, and the use of past experience and common sense to make reasonable assumptions about unspecified resources and constraints. To address these problems, we have developed a system for scheduling based on uncertain data [1][2][3][4][5][6], which has been part of the RADAR project (www.radar.cs.cmu.edu) at Carnegie Mellon University, aimed at building a software agent for assisting an office manager.…”