We present an algorithm for planning with time and resources based on heuristic search. The algorithm minimizes makespan using an admissible heuristic derived automatically from the problem instance. Estimators for resource consumption are derived in the same way. The goals are twofold: To show the flexibility of the heuristic search approach to planning and to develop a planner that combines expressivity and performance. The two main issues are the definition of regression in the temporal setting and the definition of the heuristic for estimating completion time. A number of experiments are presented for assessing the performance of the resulting planner. 1

Most recent strides in scaling up planning have centered around two competing themes--disjunctive planners, exemplified by Graphplan, and heuristic state search planners, exemplified by UNPOP, HSP and HSP-r. In this paper, we present a novel approach for successfully harnessing the advantages of the two competing paradigms to develop planners that are significantly more powerful than either of the approaches. Specifically, we show that the polynomial-time planning graph structure that the Graphplan builds provides a rich substrate for deriving a family of highly effective heuristics for guiding state space search as well as CSP-style search. The main leverage provided by the planning graph structure is a systematic and graded way to take subgoal interactions into account in designing state space heuristics.

In this paper we present a method for extracting important intermediate planning goals, and for finding orders between them which can then be used during planning. We have implemented this method and have integrated it with an example planning system, and in the paper we present results that support our expectation that these orders can lead to improved planning performance (both in terms of speed and plan quality). 1 Introduction A number of methods have been proposed for identifying orders between goals in a planning problem, the idea being to reduce the inherent complexity of the problem by partitioning it into more manageable chunks. If an order can be identified between a set of goals then this can be used to focus the planner on achieving goals that are placed earlier in the order. The particular type of goal orders that we are interested in, in this paper, have been described as reasonable orders [6] and the central idea behind them is this: a pair of goals A and B can be orde...

Most recent strides in scaling up planning have centered around two competing themes--disjunctive planners, exemplified by Graphplan, and heuristic state search planners, exemplified by HSP and HSPR.

Graphplan and heuristic state space planners such as HSP-R and UNPOP are currently two of the most effective approaches for solving classical planning problems. These approaches have hither-to been seen as largely orthogonal. In this paper, we show that the planning graph structure that Graphplan builds in polynomial time, provides a rich substrate for deriving more effective heuristics for state space planners. Specifically, we show that the heuristics used by planners such as HSP-R and UNPOP do badly in several domains due to their failure to consider the interactions between subgoals, and that the mutex information in the planning graph captures exactly this interaction information. We develop several families of heuristics, some aimed at search speed and others at optimality of solutions. Our empirical studies show that our heuristics significantly out-perform the existing state space heuristics. 1 Introduction The last few years have seen a number of attractive a...

The Graphplan algorithm for generating optimal make-span plans containing parallel sets of actions remains one of the most effective ways to generate such plans. However, despite enhancements on a range of fronts, the approach is currently dominated in terms of speed, by state space planners that employ distance-based heuristics to quickly generate serial plans. We report on a family of strategies that employ available memory to construct a search trace so as to learn from various aspects of Graphplan’s iterative search episodes in order to expedite search in subsequent episodes. The planning approaches can be partitioned into two classes according to the type and extent of search experience captured in the trace. The planners using the more aggressive tracing method are able to avoid much of Graphplan’s redundant search effort, while planners in the second class trade off this aspect in favor of a much higher degree of freedom than Graphplan in traversing the space of ‘states ’ generated during regression search on the planning graph. The tactic favored by the second approach, exploiting the search trace to transform the depthfirst, IDA * nature of Graphplan’s search into an iterative state space view, is shown to be the more powerful. We demonstrate that distance-based, state space heuristics can be adapted to informed traversal of the search trace used by the second class of planners and develop an augmentation of these heuristics targeted specifically at planning graph search. Guided by such a heuristic, the step-optimal version of planner in this class clearly dominates even a highly enhanced version of Graphplan. By adopting beam search on the search trace we then show that virtually optimal parallel plans can be generated at speeds quite competitive with a state-of-the-art heuristic state space planner. 1

We identify important intermediate planning goals, which we refer to as landmarks and present a method for extracting landmarks from an input planning problem. We also present a method for finding orders between landmarks which can be used during planning. These methods have been implemented and integrated with an example planning system. In the paper we present results that support our expectation that these orders can lead to improved planning performance (both in terms of speed and plan quality). Key Words: Planning, Goal orders, Domain analysis 1