Heuristic search strategies for multiobjective state space search

Increasing interest has been devoted to Planning as Heuristic Search over the years. Intense research has focused on deriving fast and accurate heuristics for domain-independent planning. This paper reports on an extensive survey and analysis of research work related to heuristic derivation techniques for state space search. Survey results reveal that heuristic techniques have been extensively applied in many efficient planners and result in impressive performances. We extend the survey analysis to suggest promising avenues for future research in heuristic derivation and heuristic search techniques.

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The FF Planning System: Fast Plan Generation Through Heuristic Search

Journal of Artificial Intelligence Research ◽

10.1613/jair.855 ◽

2001 ◽

Vol 14 ◽

pp. 253-302 ◽

Cited By ~ 513

Author(s):

J. Hoffmann ◽

B. Nebel

Keyword(s):

State Space ◽

Heuristic Search ◽

Search Strategy ◽

Search Space ◽

Systematic Search ◽

Hill Climbing ◽

Planning System ◽

Plan Generation ◽

State Space Search ◽

Algorithmic Techniques

We describe and evaluate the algorithmic techniques that are used in the FF planning system. Like the HSP system, FF relies on forward state space search, using a heuristic that estimates goal distances by ignoring delete lists. Unlike HSP's heuristic, our method does not assume facts to be independent. We introduce a novel search strategy that combines hill-climbing with systematic search, and we show how other powerful heuristic information can be extracted and used to prune the search space. FF was the most successful automatic planner at the recent AIPS-2000 planning competition. We review the results of the competition, give data for other benchmark domains, and investigate the reasons for the runtime performance of FF compared to HSP.

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Subgoaling Techniques for Satisficing and Optimal Numeric Planning

Journal of Artificial Intelligence Research ◽

10.1613/jair.1.11875 ◽

2020 ◽

Vol 68 ◽

pp. 691-752

Author(s):

Enrico Scala ◽

Patrik Haslum ◽

Sylvie Thiébaux ◽

Miquel Ramirez

Keyword(s):

State Space ◽

Heuristic Search ◽

State Of The Art ◽

Planning Problem ◽

State Variables ◽

The Core ◽

State Space Search ◽

Theoretical Assumptions ◽

Good Trade ◽

Core Idea

This paper studies novel subgoaling relaxations for automated planning with propositional and numeric state variables. Subgoaling relaxations address one source of complexity of the planning problem: the requirement to satisfy conditions simultaneously. The core idea is to relax this requirement by recursively decomposing conditions into atomic subgoals that are considered in isolation. Such relaxations are typically used for pruning, or as the basis for computing admissible or inadmissible heuristic estimates to guide optimal or satis_cing heuristic search planners. In the last decade or so, the subgoaling principle has underpinned the design of an abundance of relaxation-based heuristics whose formulations have greatly extended the reach of classical planning. This paper extends subgoaling relaxations to support numeric state variables and numeric conditions. We provide both theoretical and practical results, with the aim of reaching a good trade-o_ between accuracy and computation costs within a heuristic state-space search planner. Our experimental results validate the theoretical assumptions, and indicate that subgoaling substantially improves on the state of the art in optimal and satisficing numeric planning via forward state-space search.

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