This paper challenges the prevailing pessimism about the scalability of partial order planning (POP) algorithms by presenting several novel heuristic control techniques that make them competitive with the state of the art plan synthesis algorithms. Our key insight is that the techniques responsible for the efficiency of the currently successful planners--viz., distance based heuristics, reachability analysis and disjunctive constraint handling--can also be adapted to dramatically improve the efficiency of the POP algorithm. We implement our ideas in a variant of UCPOP called REPOP # . Our empirical results show that in addition to dominating UCPOP, REPOP also convincingly outperforms Graphplan in several "parallel" domains. The plans generated by REPOP also tend to be better than those generated by Graphplan and state search planners in terms of execution flexibility. 1

Some recent works in conditional planning have proposed reachability heuristics to improve planner scalability, but many lack a formal description of the properties of their distance estimates. To place previous work in context and extend work on heuristics for conditional planning, we provide a formal basis for distance estimates between belief states. We give a definition for the distance between belief states that relies on aggregating underlying state distance measures. We give several techniques to aggregate state distances and their associated properties. Many existing heuristics exhibit a subset of the properties, but in order to provide a standardized comparison we present several generalizations of planning graph heuristics that are used in a single planner. We compliment our belief state distance estimate framework by also investigating efficient planning graph data structures that incorporate BDDs to compute the most effective heuristics. We developed two planners to serve as test-beds for our investigation. The first, CAltAlt, is a conformant regression planner that uses A * search. The second, POND, is a conditional progression planner that uses AO * search. We show the relative effectiveness of our heuristic techniques within these planners. We also compare the performance of these planners with several

The planning as heuristic search framework, initiated by the planners ASP from Bonet, Loerincs and Geffner, and HSP from Bonet and Geffner, lead to some of the most performant planners, as demonstrated in the two previous editions of the International Planning Competition. We focus in this paper on a technique introduced by Hoffmann and Nebel in the FF planning system for calculating the heuristic, based on the extraction of a solution from a planning graph computed for the relaxed problem obtained by ignoring deletes of actions. This heuristic is used in a forward-chaining search algorithm to evaluate each encountered state. As a side effect of the computation of this heuristic, more information is derived from the planning graph and its solution, namely the helpful actions which permit FF to concentrate its efforts on more promising ways, forgetting the other actions in a local search algorithm. We introduce a novel way for extracting information from the computation of the heuristic and for tackling with helpful actions, by considering the high quality of the plans computed by the heuristic function in numerous domains. For each evaluated state, we employ actions from these plans in order to find the beginning of a valid plan that can lead to a reachable state, that will often bring us closer to a solution state. The lookahead state thus calculated is then added to the list of nodes that can be chosen to be developed following the numerical value of the heuristic. We use this lookahead strategy in a complete best-first search algorithm, modified in order to take into account helpful actions by preferring nodes that can be developed with such actions over nodes that can be developed with actions that are not considered as helpful. We then provide an empirical evaluation which demonstrates that in numerous planning benchmark domains, the performance of heuristic search planning and the size of the problems that can be handled have been drastically improved, while in more “difficult” domains these strategies remain interesting even if they sometimes degrade plan quality.

Sapa is a domain-independent heuristic forward chaining planner that can handle durative actions, metric resource constraints, and deadline goals. It is designed to be capable of handling the multi-objective nature of metric temporal planning. Our technical contributions include (i) planning-graph based methods for deriving heuristics that are sensitive to both cost and makespan (ii) techniques for adjusting the heuristic estimates to take action interactions and metric resource limitations into account and (iii) a linear time greedy post-processing technique to improve execution flexibility of the solution plans. An implementation of Sapa using many of the techniques presented in this paper was one of the best domain independent planners for domains with metric and temporal constraints in the third International Planning Competition, held at AIPS-02. We describe the technical details of extracting the heuristics and present an empirical evaluation of the current implementation of Sapa. 1.

Scaling conformant planning is a problem that has received much attention of late. Many planners solve the problem as a search in the space of belief states, and some heuristic guidance techniques have been developed to estimate the distance between belief states. We claim that heuristic techniques in the past involved an ad-hoc combination of classical planning heuristics and cardinality measures. We discuss

In many real world planning scenarios, agents often do not have enough resources to achieve all of their goals. Consequently, they are forced to find plans that satisfy only a subset of the goals. Solving such partial satisfaction planning (PSP) problems poses several challenges, including an increased emphasis on modeling and handling plan quality (in terms of action costs and goal utilities). Despite the ubiquity of such PSP problems, very little attention has been paid to them in the planning community. In this paper, we start by describing a spectrum of PSP problems and focus on one of the more general PSP problems, termed PSP NET BEN-EFIT. We develop three techniques, (i) one based on integer programming, called OptiPlan, (ii) the second based on regression planning with reachability heuristics, called AltAlt ps, and (iii) the third based on anytime heuristic search for a forward state-space heuristic planner, called Sapa ps. Our empirical studies with these planners show that the heuristic planners generate plans that are comparable to the quality of plans generated by OptiPlan, while incurring only a small fraction of the cost.

Heuristic search-based planners, such as HSP 2.0, solve STRIPS-style planning problems efficiently but spend about eighty percent of their planning time on calculating the heuristic values. In this paper, we systematically evaluate alternative methods for calculating the heuristic values for HSP 2.0 and demonstrate that the resulting planning times differ substantially. HSP 2.0 calculates each heuristic value by solving a relaxed planning problem with a dynamic programming method similar to value iteration. We identify two different approaches for speeding up the calculation of heuristic values, namely to order the value updates and to reuse information from the calculation of previous heuristic values. We then show how these two approaches can be combined, resulting in our PINCH method. PINCH outperforms both of the other approaches individually as well as the methods used by HSP 1.0 and HSP 2.0 for most of the large planning problems tested. In fact, it speeds up the planning time of HSP 2.0 by up to eighty percent in several domains and, in general, the amount of savings grows with the size of the domains, allowing HSP 2.0 to solve larger planning problems than was possible before in the same amount of time and without changing its overall operation.

In this research paper, we discuss Sapa, a domain-independent heuristic forward chaining planner that can handle durative actions, metric resource constraints, and deadline goals.

We present a novel approach to learning heuristic functions for AI planning domains. Given a state, we view a relaxed plan (RP) found from that state as a relational database, which includes the current state and goal facts, the actions in the RP, and the actions ’ add and delete lists. We represent heuristic functions as linear combinations of generic features of the database, selecting features and weights using training data from solved problems in the target planning domain. Many recent competitive planners use RP-based heuristics, but focus exclusively on the length of the RP, ignoring other RP features. Since RP construction ignores delete lists, for many domains, RP length dramatically under-estimates the distance to a goal, providing poor guidance. By using features that depend on deleted facts and other RP properties, our learned heuristics can potentially capture patterns that describe where such under-estimation occurs. Experiments in the STRIPS domains of IPC 3 and 4 show that best-first search using the learned heuristic can outperform FF (Hoffmann & Nebel 2001), which provided our training data, and frequently outperforms the top performances in IPC 4.

In this paper we address the problem of post-processing position constrained plans, output by many of the recent efficient metric temporal planners, to improve their execution flexibility. Specifically, given a position constrained plan, we consider the problem of generating a partially ordered (aka "order constrained") plan that uses the same actions.