Planning and Search – Research Review

Artificial Intelligence, 2002

Most recent strides in scaling up planning have centered around two competing themesdisjunctive 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. For state space search, we develop several families of heuristics, some aimed at search speed and others at optimality of solutions, and analyze many approaches for improving the cost-quality tradeoffs offered by these heuristics. Our normalized empirical comparisons show that our heuristics handily outperform the existing state space heuristics. For CSP style search, we describe a novel way of using the planning graph structure to derive highly effective variable and value ordering heuristics. We show that these heuristics can be used to improve Graphplan's own backward search significantly. To demonstrate the effectiveness of our approach vis a vis the state-of-the-art in plan synthesis, we present AltAlt, a planner literally cobbled together from the implementations of Graphplan and state search style planners using our theory. We evaluate AltAlt on the suite of problems used in the recent AIPS-2000 planning competition. The results place AltAlt in the top tier of the competition planners-outperforming both Graphplan based and heuristic search based planners.  2001 Published by Elsevier Science B.V. ✩ Preliminary versions of parts of this work have been presented at AAAI: S 0 0 0 4 -3 7 0 2 ( 0 1 ) 0 0 1 5 8 -8 74 X. Nguyen et al. / Artificial Intelligence 135 (2002)

2002

We present LPG, a fast planner using local search for solving planning graphs. LPG can use various heuristics based on a parametrized objective function. These parameters weight different types of inconsistencies in the partial plan represented by the current search state, and are dynamically evaluated during search using Lagrange multipliers. LPG's basic heuristic was inspired by Walksat, which in Kautz and Selman's Blackbox can be used to solve the SAT-encoding of a planning graph. An advantage of LPG is that its heuristics exploit the structure of the planning graph, while Blackbox relies on general heuristics for SAT-problems, and requires the translation of the planning graph into propositional clauses. Another major difference is that LPG can handle action costs to produce good quality plans. This is achieved by an "anytime" process minimizing an objective function based on the number of inconsistencies in the partial plan and on its overall cost. The objective function can also take into account the number of parallel steps and the overall plan duration. Experimental results illustrate the efficiency of our approach showing, in particular, that for a set of well-known benchmark domains LPG is significantly faster than existing Graphplan-style planners. evaluated using Lagrange multipliers. LPG's basic heuristic, Walkplan, was inspired by Walksat (Selman, Kautz, and Cohen 1994), a stochastic local search procedure which in Kautz and Selman's Blackbox (1999) can be used to solve the SAT-encoding of a planning graph .

2000

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 HSP-R. In this paper, we describe a planner called AltAlt, which successfully combines the advantages of the two competing paradigms to develop a planner that is significantly more powerful than either of the approaches. AltAlt uses Graphplan's planning graph in a novel manner to derive very effective search heuristics which are then used to drive a heuristic state search planner. Al-tAlt is implemented by splicing together implementations of STAN, a state-of-the-art Graphplan implementation, and HSP-r, a heuristic search planner. We present empirical results in a variety of domains that show the significant scale-up power of our combined approach. We will also present a variety of possible optimizations for our approach, and discuss the rich connections between our work and the literature on state-space search heuristics.

2002

We present LPG, a fast planner using local search for solving planning graphs. LPG can use various heuristics based on a parametrized objective function. These parameters weight different types of inconsistencies in the partial plan represented by the current search state, and are dynamically evaluated during search using Lagrange multipliers. LPG's basic heuristic was inspired by Walksat, which in Kautz and Selman's Blackbox can be used to solve the SAT-encoding of a planning graph. An advantage of LPG is that its heuristics exploit the structure of the planning graph, while Blackbox relies on general heuristics for SAT-problems, and requires the translation of the planning graph into propositional clauses. Another major difference is that LPG can handle action costs to produce good quality plans. This is achieved by an "anytime" process minimizing an objective function based on the number of inconsistencies in the partial plan and on its overall cost. The objective function can also take into account the number of parallel steps and the overall plan duration. Experimental results illustrate the efficiency of our approach showing, in particular, that for a set of well-known benchmark domains LPG is significantly faster than existing Graphplan-style planners. evaluated using Lagrange multipliers. LPG's basic heuristic, Walkplan, was inspired by Walksat (Selman, Kautz, and Cohen 1994), a stochastic local search procedure which in Kautz and Selman's Blackbox (1999) can be used to solve the SAT-encoding of a planning graph .

2020

Recent breakthroughs in the field of AI planning such as the Identidem and Marvin planners support the creation of more advanced and realistic representations of real-world domains. It is well-known that an adequate local search strategy can help to solve increasingly complicated Planning Domain Definition Language problems. Contemporary planners, however, strive to find a balance between the traditional greedy search and a certain degree of randomness. The aim of this work is thus to introduce a new planner that combines applicable local search techniques in a novel way not explored before to enhance the performance of the existing JavaFF planner. The new proposed planner is based on the principle of local beam search combining different successor selection methods, macros and restarts. Experimental results show that the new planner can solve considerably more problems and often within a shorter time compared to its predecessor JavaFF. Our planner could find its practical utilizati...

In the last decade three main ideas have revolutionized the Artificial Intelligence (AI) planning methods: the plan graph search structure, planning as satisfiability and the search guided by a heuristic function calculated from a relaxed plan. In this paper we present a new planner, named HPP (Heuristic Progressive Planner), that has been implemented using several ideas extracted from other heuristic planners. However, HPP includes a new module (analysis reachability module) that is able to exclude irrelevant domain-dependent operators for the planning process. The analysis performed by this module avoids to expand several parts of the search tree allowing to solve more problems. Finally, the HPP planner has been incorporated into a distributed multi-agent system to allow exploring in parallel subsets of the search space.

2007

Forward-chaining heuristic search is a well-established and popular paradigm for domain-independent planning. Its effectiveness relies on the heuristic information provided by a state evaluator, and the search algorithm used with this in order to solve the problem. This paper presents a new stochastic local-search algorithm for forward-chaining planning. The algorithm is used as the basis of a planner in conjunction with FF's Relaxed Planning Graph heuristic. Our approach is unique in that localised restarts are used, returning to the start of plateaux and saddle points, as well as global restarts to the initial state. The majority of the search time when using FF's 'Enforced Hill Climbing' is spent using breadth-first search to escape local minima. Our localised restarts, in conjunction with stochastic search, serve to replace this expensive breadth-first search step. We also describe an extended search neighbourhood incorporating non-helpful actions and the 'lookahead' states used in YAHSP. Making use of nonhelpful actions and stochastic search allows us to restart the local-search from the initial state when dead-ends are encountered; rather than resorting to best-first search. We present analyses to demonstrate the effectiveness of our restart strategies, along with results that show the new planning algorithm is effective across a range of domains.

Computational Intelligence, 2005

One of the most promising trends in Domain-Independent AI Planning, nowadays, is state-space heuristic planning. The planners of this category construct general but efficient heuristic functions, which are used as a guide to traverse the state space either in a forward or in a backward direction. Although specific problems may favor one or the other direction, there is no clear evidence why any of them should be generally preferred. This paper presents Hybrid-AcE, a domain-independent planning system that combines search in both directions utilizing a complex criterion that monitors the progress of the search, to switch between them. Hybrid AcE embodies two powerful domain-independent heuristic functions extending one of the AcE planning systems. Moreover, the system is equipped with a fact-ordering technique and two methods for problem simplification that limit the search space and guide the algorithm to the most promising states. The bi-directional system has been tested on a variety of problems adopted from the AIPS planning competitions with quite promising results.

Proceedings of the International Symposium on Combinatorial Search

Heuristic search is a successful approach to cost-optimal planning. Bidirectional heuristic search algorithms have been around for a long time, but only recent advances have led to algorithms like BAE* that have the potential to outperform unidirectional heuristic search algorithms like A* in practice. In this work, we analyze BAE* for classical planning and the challenges associated with the underlying assumption of an explicit state representation. We show that it is crucial to use mutexes and reachability analysis to reduce the potentially exponential number of goal states, which makes it possible to create an explicit representation of a reversed planning task that can be used for the backward search of BAE*. Our empirical evaluation shows that BAE* solves more instances than A* in multiple domains with significantly fewer node expansions, demonstrating the usefulness of BAE* in planning.

2020

Although symbolic bidirectional search is successful in optimal classical planning, state-of-the-art satisficing planners do not use bidirectional search. Previous bidirectional search planners for satisficing planning behaved similarly to a trivial portfolio, which independently executes forward and backward search without the desired “meet-in-themiddle” behavior of bidirectional search where the forward and backward search frontiers intersect at some point relatively far from the forward and backward start states. In this paper, we propose Top-to-Top Bidirectional Search (TTBS), a new bidirectional search strategy with front-tofront heuristic evaluation. We show that TTBS strongly exhibits “meet-in-the-middle” behavior and can solve instances solved by neither forward nor backward search on a number of domains.