Operator Selection While Planning Under Uncertainty

TL;DR

This paper presents U-Plan, a planning system that constructs and manages multiple plans under uncertainty using a best-first search strategy guided by decision-theoretic measures, enabling effective planning in incomplete and uncertain environments.

Contribution

It introduces a novel planning approach that constructs multiple possible world plans and merges them into a super-plan with knowledge acquisition, improving decision-making under uncertainty.

Findings

U-Plan effectively generates multiple plans for uncertain environments.

The system uses a decision-theoretic measure to guide search and plan selection.

Super-plans incorporate knowledge acquisition for dynamic decision-making.

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 de scribing the environment, characterizes it using a Dempster-Shafer interval, and generates a set of possible world states. Plan construction takes place in an abstraction hierarchy where strategic decisions are made before tactical decisions. Search through this abstraction hierarchy is guided by a quantitative measure (expected fulfillment) based on decision theory. The search strategy is best first with the provision to update expected fulfillment 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.