scholarly journals Front-to-Front Heuristic Search for Satisficing Classical Planning

2020 ◽  
Author(s):  
Ryo Kuroiwa ◽  
Alex Fukunaga
Keyword(s):  
Heuristic Search ◽  
Search Strategy ◽  
State Of The Art ◽  
Heuristic Evaluation ◽  
Bidirectional Search

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.

scholarly journals Bidirectional Heuristic Search Reconsidered

1997 ◽  
Vol 7 ◽  
pp. 283-317 ◽  
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.

scholarly journals On the Completeness of Pruning Techniques for Planning with Conditional Effects

2013 ◽  
Vol 2013 ◽  
pp. 1-6
Author(s):  
Dunbo Cai ◽  
Sheng Xu ◽  
Tongzhou Zhao ◽  
Yanduo Zhang
Keyword(s):  
Heuristic Search ◽  
State Of The Art ◽  
The State ◽  
Search Procedure ◽  
Conditional Effects ◽  
Relevance Analysis ◽  
Pruning Strategy ◽  
Pruning Techniques

Pruning techniques and heuristics are two keys to the heuristic search-based planning. Thehelpful actionspruning (HAP) strategy andrelaxed-plan-based heuristicsare two representatives among those methods and are still popular in the state-of-the-art planners. Here, we present new analyses on the properties of HAP. Specifically, we show new reasons for which HAP can cause incompleteness of a search procedure. We prove that, in general, HAP is incomplete for planning with conditional effects if factored expansions of actions are used. To preserve completeness, we propose a pruning strategy that is based onrelevance analysisandconfrontation. We will show that bothrelevance analysisandconfrontationare necessary. We call it theconfrontation and goal relevant actionspruning (CGRAP) strategy. However, CGRAP is computationally hard to be exactly computed. Therefore, we suggest practical approximations from the literature.

scholarly journals Heuristic Search Strategy for Transforming Microstructural Patterns to Optimal Copolymerization Recipes

2018 ◽  
Vol 27 (2) ◽  
pp. 1700088 ◽  
Author(s):  
Yousef Mohammadi ◽  
Mohammad Reza Saeb ◽  
Alexander Penlidis
Keyword(s):  
Heuristic Search ◽  
Search Strategy

scholarly journals A Unifying View on Individual Bounds and Heuristic Inaccuracies in Bidirectional Search

2020 ◽  
Vol 34 (03) ◽  
pp. 2327-2334
Author(s):  
Vidal Alcázar ◽  
Pat Riddle ◽  
Mike Barley
Keyword(s):  
Lower Bound ◽  
Heuristic Search ◽  
Search Algorithm ◽  
Substantial Improvement ◽  
Full Potential ◽  
The Past ◽  
Heuristic Search Algorithms ◽  
Bidirectional Search ◽  
Order Of Magnitude ◽  
The Cost

In the past few years, new very successful bidirectional heuristic search algorithms have been proposed. Their key novelty is a lower bound on the cost of a solution that includes information from the g values in both directions. Kaindl and Kainz (1997) proposed measuring how inaccurate a heuristic is while expanding nodes in the opposite direction, and using this information to raise the f value of the evaluated nodes. However, this comes with a set of disadvantages and remains yet to be exploited to its full potential. Additionally, Sadhukhan (2013) presented BAE∗, a bidirectional best-first search algorithm based on the accumulated heuristic inaccuracy along a path. However, no complete comparison in regards to other bidirectional algorithms has yet been done, neither theoretical nor empirical. In this paper we define individual bounds within the lower-bound framework and show how both Kaindl and Kainz's and Sadhukhan's methods can be generalized thus creating new bounds. This overcomes previous shortcomings and allows newer algorithms to benefit from these techniques as well. Experimental results show a substantial improvement, up to an order of magnitude in the number of necessarily-expanded nodes compared to state-of-the-art near-optimal algorithms in common benchmarks.

Collaborative correlation filters for real-time tracking with spatial constraint

2019 ◽  
Vol 17 (03) ◽  
pp. 1950012
Author(s):  
Lifang Zhou ◽  
Hongmei Li ◽  
Weisheng Li ◽  
Bangjun Lei ◽  
Lu Wang
Keyword(s):  
Adaptive Sampling ◽  
Search Strategy ◽  
State Of The Art ◽  
Computational Cost ◽  
Background Information ◽  
Spatial Constraint ◽  
Correlation Filters ◽  
Computational Costs ◽  
Real Time Tracking ◽  
Selection Factor

Accurate scale estimation of the target plays an important role in object tracking. Most state-of-the-art methods estimate the target size by employing an exhaustive scale search. These methods can achieve high accuracy but suffer significantly from large computational cost. In this paper, we first propose an adaptive scale search strategy with the scale selection factor instead of an exhaustive scale search. This proposed strategy contributes to reducing computational costs by adaptive sampling. Furthermore, the boundary effects of correlation filters are suppressed by investigating background information so that the accuracy of the proposed tracker can be boosted. Experiments’ empirical evaluations of 61 challenging benchmark sequences demonstrate that the overall tracking performance of the proposed tracker is very successfully improved. Moreover, our method obtains the top rank in performance by outperforming 17 state-of-the-art trackers on OTB2013.

scholarly journals A Methodology for Classifying Search Operators as Intensification or Diversification Heuristics

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Jorge A. Soria-Alcaraz ◽  
Gabriela Ochoa ◽  
Andres Espinal ◽  
Marco A. Sotelo-Figueroa ◽  
Manuel Ornelas-Rodriguez ◽  
...  
Keyword(s):  
Search Strategy ◽  
State Of The Art ◽  
Iterated Local Search ◽  
Search Process ◽  
Local Optima ◽  
Course Timetabling ◽  
Methodological Rigor ◽  
Improved Performance ◽  
Course Timetabling Problem

Selection hyper-heuristics are generic search tools that dynamically choose, from a given pool, the most promising operator (low-level heuristic) to apply at each iteration of the search process. The performance of these methods depends on the quality of the heuristic pool. Two types of heuristics can be part of the pool: diversification heuristics, which help to escape from local optima, and intensification heuristics, which effectively exploit promising regions in the vicinity of good solutions. An effective search strategy needs a balance between these two strategies. However, it is not straightforward to categorize an operator as intensification or diversification heuristic on complex domains. Therefore, we propose an automated methodology to do this classification. This brings methodological rigor to the configuration of an iterated local search hyper-heuristic featuring diversification and intensification stages. The methodology considers the empirical ranking of the heuristics based on an estimation of their capacity to either diversify or intensify the search. We incorporate the proposed approach into a state-of-the-art hyper-heuristic solving two domains: course timetabling and vehicle routing. Our results indicate improved performance, including new best-known solutions for the course timetabling problem.

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