scholarly journals Tie-Breaking Strategies for Cost-Optimal Best First Search

2017 ◽  
Vol 58 ◽  
pp. 67-121 ◽  
Author(s):  
Masataro Asai ◽  
Alex Fukunaga
Keyword(s):  
Search Algorithm ◽  
Search Space ◽  
Search Algorithms ◽  
Optimal Search ◽  
Diversification Strategy ◽  
Algorithm Performance ◽  
New Class ◽  
New Strategy ◽  
Best First Search ◽  
New Framework

Best-first search algorithms such as A* need to apply tie-breaking strategies in order to decide which node to expand when multiple search nodes have the same evaluation score. We investigate and improve tie-breaking strategies for cost-optimal search using A*. We first experimentally analyze the performance of common tie-breaking strategies that break ties according to the heuristic value of the nodes. We find that the tie-breaking strategy has a significant impact on search algorithm performance when there are 0-cost operators that induce large plateau regions in the search space. Based on this, we develop two new classes of tie-breaking strategies. We first propose a depth diversification strategy which breaks ties according to the distance from the entrance to the plateau, and then show that this new strategy significantly outperforms standard strategies on domains with 0-cost actions. Next, we propose a new framework for interpreting A* search as a series of satisficing searches within plateaus consisting of nodes with the same f-cost. Based on this framework, we investigate a second, new class of tie-breaking strategy, a multi-heuristic tie-breaking strategy which embeds inadmissible, distance-to-go variations of various heuristics within an admissible search. This is shown to further improve the performance in combination with the depth metric.

scholarly journals Search Progress and Potentially Expanded States in Greedy Best-First Search

2018 ◽  
Author(s):  
Manuel Heusner ◽  
Thomas Keller ◽  
Malte Helmert
Keyword(s):  
Classical Result ◽  
Search Behavior ◽  
Optimal Solution ◽  
Search Space ◽  
High Water ◽  
Search Algorithms ◽  
Clear Understanding ◽  
Optimal Search ◽  
High Water Mark ◽  
Best First Search

A classical result in optimal search shows that A* with an admissible and consistent heuristic expands every state whose f-value is below the optimal solution cost and no state whose f-value is above the optimal solution cost. For satisficing search algorithms, a similarly clear understanding is currently lacking. We examine the search behavior of greedy best-first search (GBFS) in order to make progress towards such an understanding. We introduce the concept of high-water mark benches, which separate the search space into areas that are searched by a GBFS algorithm in sequence. High-water mark benches allow us to exactly determine the set of states that are expanded by at least one GBFS tie-breaking strategy and give us a clearer understanding of search progress.

scholarly journals Enhanced Simplified Memory-bounded A Star (SMA*+)

10.29007/v7zc ◽  
2018 ◽  
Author(s):  
Justin Lovinger ◽  
Xiaoqin Zhang
Keyword(s):  
Explicit Memory ◽  
Search Algorithm ◽  
A Priori ◽  
Search Space ◽  
Search Algorithms ◽  
Search Performance ◽  
Heuristic Function ◽  
Optimal Search ◽  
Comparison Results ◽  
Memory Efficient

In 1992, Stuart Russell briefly introduced a series of memory efficient optimal search algorithms. Among which is the Simplified Memory-bounded A Star (SMA*) algorithm, unique for its explicit memory bound. Despite progress in memory efficient A Star variants, search algorithms with explicit memory bounds are absent from progress. SMA* remains the premier memory bounded optimal search algorithm. In this paper, we present an enhanced version of SMA* (SMA*+), providing a new open list, simplified implementation, and a culling heuristic function, which improves search performance through a priori knowledge of the search space. We present benchmark and comparison results with state-of-the-art optimal search algorithms, and examine the performance characteristics of SMA*+.

scholarly journals Accelerating backtrack search with a best-first-search strategy

2014 ◽  
Vol 24 (4) ◽  
pp. 901-916
Author(s):  
Zoltán Ádám Mann ◽  
Tamás Szép
Keyword(s):  
Search Strategy ◽  
Search Algorithm ◽  
Search Space ◽  
New Approach ◽  
Backtrack Search ◽  
Speed Up ◽  
Hard Problems ◽  
Best First Search ◽  
Problem Instances ◽  
Np Hard Problems

Abstract Backtrack-style exhaustive search algorithms for NP-hard problems tend to have large variance in their runtime. This is because “fortunate” branching decisions can lead to finding a solution quickly, whereas “unfortunate” decisions in another run can lead the algorithm to a region of the search space with no solutions. In the literature, frequent restarting has been suggested as a means to overcome this problem. In this paper, we propose a more sophisticated approach: a best-firstsearch heuristic to quickly move between parts of the search space, always concentrating on the most promising region. We describe how this idea can be efficiently incorporated into a backtrack search algorithm, without sacrificing optimality. Moreover, we demonstrate empirically that, for hard solvable problem instances, the new approach provides significantly higher speed-up than frequent restarting.

scholarly journals Open-pFind enables precise, comprehensive and rapid peptide identification in shotgun proteomics

2018 ◽  
Author(s):  
Hao Chi ◽  
Chao Liu ◽  
Hao Yang ◽  
Wen-Feng Zeng ◽  
Long Wu ◽  
...  
Keyword(s):  
Large Scale ◽  
Search Algorithm ◽  
Large Fraction ◽  
Olfactory Receptors ◽  
Peptide Identification ◽  
Shotgun Proteomics ◽  
Isotopic Labeling ◽  
Search Space ◽  
Global Scale ◽  
Search Algorithms

ABSTRACTShotgun proteomics has grown rapidly in recent decades, but a large fraction of tandem mass spectrometry (MS/MS) data in shotgun proteomics are not successfully identified. We have developed a novel database search algorithm, Open-pFind, to efficiently identify peptides even in an ultra-large search space which takes into account unexpected modifications, amino acid mutations, semi- or non-specific digestion and co-eluting peptides. Tested on two metabolically labeled MS/MS datasets, Open-pFind reported 50.5‒117.0% more peptide-spectrum matches (PSMs) than the seven other advanced algorithms. More importantly, the Open-pFind results were more credible judged by the verification experiments using stable isotopic labeling. Tested on four additional large-scale datasets, 70‒85% of the spectra were confidently identified, and high-quality spectra were nearly completely interpreted by Open-pFind. Further, Open-pFind was over 40 times faster than the other three open search algorithms and 2‒3 times faster than three restricted search algorithms. Re-analysis of an entire human proteome dataset consisting of ∼25 million spectra using Open-pFind identified a total of 14,064 proteins encoded by 12,723 genes by requiring at least two uniquely identified peptides. In this search results, Open-pFind also excelled in an independent test for false positives based on the presence or absence of olfactory receptors. Thus, a practical use of the open search strategy has been realized by Open-pFind for the truly global-scale proteomics experiments of today and in the future.

scholarly journals Effective Heuristics for Suboptimal Best-First Search

2016 ◽  
Vol 57 ◽  
pp. 273-306 ◽  
Author(s):  
Christopher Wilt ◽  
Wheeler Ruml
Keyword(s):  
Sustained Attention ◽  
Heuristic Search ◽  
Rank Correlation ◽  
Search Algorithms ◽  
Optimal Solutions ◽  
Heuristic Function ◽  
Optimal Search ◽  
Heuristic Search Algorithms ◽  
Best First Search ◽  
Goal Distance

Suboptimal heuristic search algorithms such as weighted A* and greedy best-first search are widely used to solve problems for which guaranteed optimal solutions are too expensive to obtain. These algorithms crucially rely on a heuristic function to guide their search. However, most research on building heuristics addresses optimal solving. In this paper, we illustrate how established wisdom for constructing heuristics for optimal search can fail when considering suboptimal search. We consider the behavior of greedy best-first search in detail and we test several hypotheses for predicting when a heuristic will be effective for it. Our results suggest that a predictive characteristic is a heuristic's goal distance rank correlation (GDRC), a robust measure of whether it orders nodes according to distance to a goal. We demonstrate that GDRC can be used to automatically construct abstraction-based heuristics for greedy best-first search that are more effective than those built by methods oriented toward optimal search. These results reinforce the point that suboptimal search deserves sustained attention and specialized methods of its own.

scholarly journals Algorithms for Generating Ordered Solutions for Explicit AND/OR Structures

2012 ◽  
Vol 44 ◽  
pp. 275-333 ◽  
Author(s):  
P. Ghosh ◽  
A. Sharma ◽  
P.P. Chakrabarti ◽  
P. Dasgupta
Keyword(s):  
Search Algorithm ◽  
Search Space ◽  
Compact Representation ◽  
Implicit Representation ◽  
Knowledge Based ◽  
The Matrix ◽  
Multiplication Problem ◽  
Best First Search ◽  
Alternative Solutions ◽  
Potential Use

We present algorithms for generating alternative solutions for explicit acyclic AND/OR structures in non-decreasing order of cost. The proposed algorithms use a best first search technique and report the solutions using an implicit representation ordered by cost. In this paper, we present two versions of the search algorithm -- (a) an initial version of the best first search algorithm, ASG, which may present one solution more than once while generating the ordered solutions, and (b) another version, LASG, which avoids the construction of the duplicate solutions. The actual solutions can be reconstructed quickly from the implicit compact representation used. We have applied the methods on a few test domains, some of them are synthetic while the others are based on well known problems including the search space of the 5-peg Tower of Hanoi problem, the matrix-chain multiplication problem and the problem of finding secondary structure of RNA. Experimental results show the efficacy of the proposed algorithms over the existing approach. Our proposed algorithms have potential use in various domains ranging from knowledge based frameworks to service composition, where the AND/OR structure is widely used for representing problems.

scholarly journals Simulated Annealing with Exploratory Sensing for Global Optimization

Algorithms ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 230
Author(s):  
Majid Almarashi ◽  
Wael Deabes ◽  
Hesham H. Amin ◽  
Abdel-Rahman Hedar
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Initial Temperature ◽  
Search Algorithm ◽  
Random Search ◽  
Search Space ◽  
Search Algorithms ◽  
Two Phases ◽  
Number Of Iterations

Simulated annealing is a well-known search algorithm used with success history in many search problems. However, the random walk of the simulated annealing does not benefit from the memory of visited states, causing excessive random search with no diversification history. Unlike memory-based search algorithms such as the tabu search, the search in simulated annealing is dependent on the choice of the initial temperature to explore the search space, which has little indications of how much exploration has been carried out. The lack of exploration eye can affect the quality of the found solutions while the nature of the search in simulated annealing is mainly local. In this work, a methodology of two phases using an automatic diversification and intensification based on memory and sensing tools is proposed. The proposed method is called Simulated Annealing with Exploratory Sensing. The computational experiments show the efficiency of the proposed method in ensuring a good exploration while finding good solutions within a similar number of iterations.

scholarly journals Online Bridged Pruning for Real-Time Search with Arbitrary Lookaheads

2017 ◽  
Author(s):  
Carlos Hernandez ◽  
Adi Botea ◽  
Jorge A. Baier ◽  
Vadim Bulitko
Keyword(s):  
Real Time ◽  
Recent Progress ◽  
State Of The Art ◽  
Search Algorithm ◽  
Search Space ◽  
Poor Quality ◽  
Search Algorithms ◽  
Arbitrary Size ◽  
Order Of Magnitude ◽  
And Robotics

Real-time search algorithms are relevant to time-sensitive decision-making domains such as video games and robotics. In such settings, the agent is required to decide on each action under a constant time bound, regardless of the search space size. Despite recent progress, poor-quality solutions can be produced mainly due to state re-visitation. Different techniques have been developed to reduce such a re-visitation with state pruning showing promise. In this paper, we propose a novel pruning approach applicable to the wide class of real-time search algorithms. Given a local search space of arbitrary size, our technique aggressively prunes away all states in its interior, possibly adding new edges to maintain the connectivity of the search space frontier. An experimental evaluation shows that our pruning often improves the performance of a base real-time search algorithm by over an order of magnitude. This allows our implemented system to outperform state-of-the-art real-time search algorithms used in the evaluation.

scholarly journals Online Relaxation Refinement for Satisficing Planning: On Partial Delete Relaxation, Complete Hill-Climbing, and Novelty Pruning

2022 ◽  
Vol 73 ◽  
Author(s):  
Maximilian Fickert ◽  
Jörg Hoffmann
Keyword(s):  
Local Minimum ◽  
State Of The Art ◽  
Convergence Property ◽  
Search Space ◽  
Search Algorithms ◽  
Hill Climbing ◽  
Ai Planning ◽  
Best First Search ◽  
Refinement Procedure ◽  
Hill Climbing Algorithms

In classical AI planning, heuristic functions typically base their estimates on a relaxation of the input task. Such relaxations can be more or less precise, and many heuristic functions have a refinement procedure that can be iteratively applied until the desired degree of precision is reached. Traditionally, such refinement is performed offline to instantiate the heuristic for the search. However, a natural idea is to perform such refinement online instead, in situations where the heuristic is not sufficiently accurate. We introduce several online-refinement search algorithms, based on hill-climbing and greedy best-first search. Our hill-climbing algorithms perform a bounded lookahead, proceeding to a state with lower heuristic value than the root state of the lookahead if such a state exists, or refining the heuristic otherwise to remove such a local minimum from the search space surface. These algorithms are complete if the refinement procedure satisfies a suitable convergence property. We transfer the idea of bounded lookaheads to greedy best-first search with a lightweight lookahead after each expansion, serving both as a method to boost search progress and to detect when the heuristic is inaccurate, identifying an opportunity for online refinement. We evaluate our algorithms with the partial delete relaxation heuristic hCFF, which can be refined by treating additional conjunctions of facts as atomic, and whose refinement operation satisfies the convergence property required for completeness. On both the IPC domains as well as on the recently published Autoscale benchmarks, our online-refinement search algorithms significantly beat state-of-the-art satisficing planners, and are competitive even with complex portfolios.

Efficient Singleton Consistency by Combining Forward Checking and Bound Consistency

2014 ◽  
Vol 23 (04) ◽  
pp. 1460017
Author(s):  
Jinsong Guo ◽  
Hongbo Li ◽  
Zhanshan Li ◽  
Yonggang Zhang ◽  
Xianghua Jia
Keyword(s):  
Constraint Satisfaction ◽  
Search Algorithm ◽  
Computational Cost ◽  
Search Space ◽  
Search Algorithms ◽  
Constraint Satisfaction Problems ◽  
Local Consistency ◽  
Arc Consistency ◽  
Strong Bound

Maintaining local consistencies can improve the efficiencies of the search algorithms solving constraint satisfaction problems (CSPs). Comparing with arc consistency which is the most widely used local consistency, stronger local consistencies can make the search space smaller while they require higher computational cost. In this paper, we make an attempt on the compromise between the pruning ability and the computational cost. A new local consistency called singleton strong bound consistency (SSBC) and its light version, light SSBC, are proposed. The search algorithm maintaining light SSBC can outperform MAC on a considerable number of problems.

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