Refinement Planning: Status and Prospectus

Abstract

Most current-day AI planning systems operate by iteratively refining a partial plan until it meets the goal requirements. In the past five years, significant progress has been made in our understanding of the spectrum and capabilities of such refinement planners. In this talk, I will summarize this understanding in terms of a unified framework for refinement planning and discuss several current research directions. Introduction Developing automated methods for generating and reasoning about plans and schedules, whether in aid of autonomous or human agents, has been part and parcel of AI research from the beginning. The need for planning arises naturally when an agent is interested in controlling the evolution of its environment. Algorithmically, a planning problem has as input a set of possible courses of actions, a predictive model for the underlying dynamics, and a performance measure for evaluating the courses of action. The output or solution is one or more courses of action that ...