Operator selection while planning under uncertainty

  • Authors:

  • Todd Michael Mansell;Grahame Smith

  • Affiliations:

  • Materials Research Laboratory, Melbourne, Australia and Department of Computer Science, The University of Melbourne, Parkville, Victoria, Australia;Cooperative Research Centre for Intelligent Decision Systems, Carlton, Victoria, Australia

  • Venue:
  • UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
  • Year:
  • 1994

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Abstract

This paper describes the best first search strategy used by U-Plan (Mansell 1993a), a planning system that constructs quantitatively ranked plans given an incomplete description of an uncertain environment. U-Plan uses uncertain and incomplete evidence describing the environment, characterises it using a Dempster-Shafer interval, and generates a set of possible world states. Plan construction takes place in an abslraction hierarchy where strategic decisions are made before tactical decisions. Search through this abstraction hierarchy is guided by a quantitative measure (expected fulfilment) based on decision theory. The search strategy is best first with the provision to update expected fulfilments and review previous decisions in the light of planning developments. U-Plan generates multiple plans for multiple possible worlds, and attempts to use existing plans for new world situations. A super-plan is then constructed, based on merging the set of plans and appropriately timed knowledge acquisition operators, which are used to decide between plan alternatives during plan execution.