On Reasonable and Forced Goal Orderings and their Use in an Agenda-Driven Planning Algorithm

TL;DR

This paper introduces formal definitions and computational methods for reasonable and forced goal orderings in AI planning, demonstrating how goal agendas can improve planning efficiency by decomposing complex problems into manageable subproblems.

Contribution

It formally defines two types of goal orderings, develops methods to compute them, and shows how to use these orderings to create goal agendas that enhance planning efficiency.

Findings

Goal orderings can be computed efficiently with polynomial overhead.

Using goal agendas can significantly reduce planning complexity.

Empirical results show speedups in the IPP planner when using goal agendas.

Abstract

The paper addresses the problem of computing goal orderings, which is one of the longstanding issues in AI planning. It makes two new contributions. First, it formally defines and discusses two different goal orderings, which are called the reasonable and the forced ordering. Both orderings are defined for simple STRIPS operators as well as for more complex ADL operators supporting negation and conditional effects. The complexity of these orderings is investigated and their practical relevance is discussed. Secondly, two different methods to compute reasonable goal orderings are developed. One of them is based on planning graphs, while the other investigates the set of actions directly. Finally, it is shown how the ordering relations, which have been derived for a given set of goals G, can be used to compute a so-called goal agenda that divides G into an ordered set of subgoals. Any planner can then, in principle, use the goal agenda to plan for increasing sets of subgoals. This can lead to an exponential complexity reduction, as the solution to a complex planning problem is found by solving easier subproblems. Since only a polynomial overhead is caused by the goal agenda computation, a potential exists to dramatically speed up planning algorithms as we demonstrate in the empirical evaluation, where we use this method in the IPP planner.