Enriching Non-Parametric Bidirectional Search Algorithms

Shperberg, Shahaf S.; Felner, Ariel; Sturtevant, Nathan R.; Shimony, Solomon Eyal; Hayoun, Avi

doi:10.1609/aaai.v33i01.33012379

Cited by 11 publications

(21 citation statements)

References 16 publications

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“…NBS (Chen et al 2017) and NBB (Alcázar, Barley, and Riddle 2019), both near-optimal algorithms. NBB is equivalent to NBS a (Shperberg et al 2019a) apart from its pseudocode; we choose NBB because its bucket-based implementation is the basis for DBS and DBBS (described below). • DVCBS and DVCBS a .…”

Section: Methodsmentioning

confidence: 99%

“…• DVCBS and DVCBS a . Developed by Shperberg et al (2019a), the reported state of the art. • GBFHS and GBFHS e .…”

Section: Methodsmentioning

confidence: 99%

“…in A* (Hart, Nilsson, and Raphael 1968) the implicit C is the minimum f value in the open list. However, keeping track of C explicitly allows implementing more complex techniques (Chen et al 2017;Barley et al 2018;Shperberg et al 2019a;2019b;Alcázar, Barley, and Riddle 2019). Both Shperberg et al (2019b) and Alcázar et al (2019) propose a high-level algorithm to keep track of C, but the former expands whole layers and the latter defines how states are alternatively expanded.…”

Section: Individual Bounds and Relative Minimum Node Valuesmentioning

confidence: 99%

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

Alcázar

Riddle

2020

AAAI

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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.

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Section: Methodsmentioning

confidence: 99%

“…• DVCBS and DVCBS a . Developed by Shperberg et al (2019a), the reported state of the art. • GBFHS and GBFHS e .…”

Section: Methodsmentioning

confidence: 99%

Section: Individual Bounds and Relative Minimum Node Valuesmentioning

confidence: 99%

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

Alcázar

Riddle

2020

AAAI

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“…Many enhancements have been proposed for IDA * (Sarkar et al 1991;Wah and Shang 1994;Burns and Ruml 2013;Stern et al 2010;Bu et al 2014;Sharon, Felner, and Sturtevant 2014;Hatem, Burns, and Ruml 2018), as well as other LM algorithms such as Dual IDA* (Zahavi et al 2008), and IBEX (Helmert et al 2019). These algorithms only store a single path and do not utilize the available memory.…”

Section: Restricted Memory Unihs Algorithmsmentioning

confidence: 99%

Iterative-deepening Bidirectional Heuristic Search with Restricted Memory

Shperberg

Danishevski

Felner

et al. 2021

ICAPS

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The field of bidirectional heuristic search has recently seen great advances. However, the subject of memory-restricted bidirectional search has not received recent attention. In this paper we introduce a general iterative deepening bidirectional heuristic search algorithm (IDBiHS) that searches simultaneously in both directions while controlling the meeting point of the search frontiers. First, we present the basic variant of IDBiHS, whose memory is linear in the search depth. We then add improvements that exploit consistency and front-to-front heuristics. Next, we move to the case where a fixed amount of memory is available to store nodes during the search and develop two variants of IDBiHS: (1) A*+IDBiHS, that starts with A* and moves to IDBiHS as soon as memory is exhausted. (2) A variant that stores partial forward frontiers until memory is exhausted and then tries to match each of them from the backward side. Finally, we experimentally compare the new algorithms to existing unidirectional and bidirectional ones. In many cases our new algorithms outperform previous ones in both node expansions and time.

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“…Consider the problem of estimating the probability that the plan makespan does not exceed a given deadline. Algorithms for solving such problems often apply operations like addition, multiplication and maximization, over random variables, see, e.g., (Shperberg, Shimony, and Felner 2017). In many cases the analysis found in the literature of the complexity of such algorithms focuses on the complexity stemming from the dependency graph and ignores the complexity that stems from the growth of the support, see, e.g., (Hagstrom 1988).…”

Section: Introductionmentioning

confidence: 99%

Optimal Approximation of Random Variables for Estimating the Probability of Meeting a Plan Deadline

Cohen

Grinshpoun

Weiss

2018

AAAI

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In planning algorithms and in other domains, there is often a need to run long computations that involve summations, maximizations and other operations on random variables, and to store intermediate results. In this paper, as a main motivating example, we elaborate on the case of estimating probabilities of meeting deadlines in hierarchical plans. A source of computational complexity, often neglected in the analysis of such algorithms, is that the support of the variables needed as intermediate results may grow exponentially along the computation. Therefore, to avoid exponential memory and time complexities, we need to trim these variables. This is similar, in a sense, to rounding intermediate results in numerical computations. Of course, to maintain the quality of algorithms, the trimming procedure should be efficient and it must maintain accuracy as much as possible. In this paper, we propose an optimal trimming algorithm with polynomial time and memory complexities for the purpose of estimating probabilities of deadlines in plans. More specifically, we show that our algorithm, given the needed size of the representation of the variable, provides the best possible approximation, where approximation accuracy is considered with a measure that fits the goal of estimating deadline meeting probabilities.

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Enriching Non-Parametric Bidirectional Search Algorithms

Cited by 11 publications

References 16 publications

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

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

Iterative-deepening Bidirectional Heuristic Search with Restricted Memory

Optimal Approximation of Random Variables for Estimating the Probability of Meeting a Plan Deadline

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