C-Focus-3: a C-Focus with a New Heuristic Search Strategy

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-the-middle'' 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-to-front 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.

Download Full-text

Heuristic Search Strategy for Transforming Microstructural Patterns to Optimal Copolymerization Recipes

Macromolecular Theory and Simulations ◽

10.1002/mats.201700088 ◽

2018 ◽

Vol 27 (2) ◽

pp. 1700088 ◽

Cited By ~ 6

Author(s):

Yousef Mohammadi ◽

Mohammad Reza Saeb ◽

Alexander Penlidis

Keyword(s):

Heuristic Search ◽

Search Strategy

Download Full-text

Bidirectional Heuristic Search Reconsidered

Journal of Artificial Intelligence Research ◽

10.1613/jair.460 ◽

1997 ◽

Vol 7 ◽

pp. 283-317 ◽

Cited By ~ 38

Author(s):

H. Kaindl ◽

G. Kainz

Keyword(s):

Heuristic Search ◽

Search Strategy ◽

Wide Spread ◽

New Approach ◽

Empirical Results ◽

Limited Memory ◽

New Approaches ◽

Bidirectional Search ◽

Long Time

The assessment of bidirectional heuristic search has been incorrect since it was first published more than a quarter of a century ago. For quite a long time, this search strategy did not achieve the expected results, and there was a major misunderstanding about the reasons behind it. Although there is still wide-spread belief that bidirectional heuristic search is afflicted by the problem of search frontiers passing each other, we demonstrate that this conjecture is wrong. Based on this finding, we present both a new generic approach to bidirectional heuristic search and a new approach to dynamically improving heuristic values that is feasible in bidirectional search only. These approaches are put into perspective with both the traditional and more recently proposed approaches in order to facilitate a better overall understanding. Empirical results of experiments with our new approaches show that bidirectional heuristic search can be performed very efficiently and also with limited memory. These results suggest that bidirectional heuristic search appears to be better for solving certain difficult problems than corresponding unidirectional search. This provides some evidence for the usefulness of a search strategy that was long neglected. In summary, we show that bidirectional heuristic search is viable and consequently propose that it be reconsidered.

Download Full-text

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.

Download Full-text