Theoretical Foundations for Abstraction-Based Probabilistic Planning
This work addresses the challenge of representing uncertainty in planning for AI researchers, but it appears incremental as it builds on existing operator-based definitions and abstraction types.
The paper tackles the problem of modeling uncertainty in decision-theoretic planning by proposing a framework based on affine-operators to construct sets of probability distributions, generalizing belief functions and interval mass assignments. It derives and proves three projection rules to illustrate the precision-complexity tradeoff in plan projection.
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.