Uncertainty in scheduling: probability, problem reduction, abstractions and the user

In most realistic scheduling situations, the information available to the decision-maker is both incomplete and uncertain. This complicates the automation of intelligent reasoning systems in the real world. The author discusses the issues involved in reasoning in uncertain environments and argues that scheduling is essentially a problem of decision-making under uncertainty. She classifies various types of uncertainty and proposes techniques to address these problems within the advanced software domain. These techniques include the use of probabilistic modelling, problem reduction, temporal abstractions and the user. Relevant issues are illustrated through an advanced scheduling system being developed at the University of Geneva. The author also mentions how these techniques relate to TOSCA, a system developed for manufacturing by AIAI.<
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