Uncertainty in Artificial Intelligence
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Theoretical Foundations for Abstraction-Based Probabilistic Planning
Vu Ha, Peter Haddawy
Abstract:
Modeling worlds and actions under uncertainty is one of the central problems in the framework of decision-theoretic planning. The representation must be general enough to capture real-world problems but at the same time it must provide a basis upon which theoretical results can be derived. The central notion in the framework we propose here is that of the affine-operator, which serves as a tool for constructing (convex) sets of probability distributions, and which can be considered as a generalization of belief functions and interval mass assignments. Uncertainty in the state of the worlds is modeled with sets of probability distributions, represented by affine-trees while actions are defined as tree-manipulators. A small set of key properties of the affine-operator is presented, forming the basis for most existing operator-based definitions of probabilistic action projection and action abstraction. We derive and prove correct three projection rules, which vividly illustrate the precision-complexity tradeoff in plan projection. Finally, we show how the three types of action abstraction identified by Haddawy and Doan are manifested in the present framework.
Keywords: Decision-theoretic planning, plan projection, action abstraction, convex sets of pro
Pages: 291-298
PS Link: http://www.cs.uwm.edu/public/vu/public/uai96-final.ps
PDF Link: /papers/96/p291-ha.pdf
BibTex:
@INPROCEEDINGS{Ha96,
AUTHOR = "Vu Ha and Peter Haddawy",
TITLE = "Theoretical Foundations for Abstraction-Based Probabilistic Planning",
BOOKTITLE = "Proceedings of the Twelfth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-96)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1996",
PAGES = "291--298"
}


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