Static and Dynamic Portfolio Methods for Optimal Planning: An Empirical Analysis

Rizzini, Mattia; Fawcett, Chris; Vallati, Mauro; Gerevini, Alfonso; Hoos, Holger H.

doi:10.1142/s0218213017600065

Cited by 13 publications

(9 citation statements)

References 18 publications

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“…A second per-instance algorithm selection approach for AI planning has been considered by Rizzini et al (2015Rizzini et al ( , 2017. Their PLANZILLA system can be seen as an application of the previously outlined * ZILLA approach (Cameron et al, 2017) to AI planning.…”

Section: Planzillamentioning

confidence: 99%

“…Using all planners that participated in the optimal track of the 2014 International Planning Competition (IPC-14), PLANZILLA was found to substantially outperform these individual planners and achieve performance close to that of the VBS (Rizzini et al, 2015(Rizzini et al, , 2017. However, when evaluated on a set of testing instances dissimilar from those used for training, it was found that dynamic algorithm scheduling approaches performed better than PLANZILLA; these approaches dynamically construct an algorithm schedule by performing multiple stages of per-instance algorithm selection, using not only features of the planning instance i to be solved, but also taking into account which component planners have already been run on i, without success, in earlier stages of the schedule.…”

Section: Planzillamentioning

confidence: 99%

See 1 more Smart Citation

Automated Algorithm Selection: Survey and Perspectives

Kerschke

Hoos

Neumann

et al. 2019

Evolutionary Computation

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317

184

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It has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different algorithms. This is particularly pronounced for computationally hard problems, where in most cases, no single algorithm defines the state of the art; instead, there is a set of algorithms with complementary strengths. This performance complementarity can be exploited in various ways, one of which is based on the idea of selecting, from a set of given algorithms, for each problem instance to be solved the one expected to perform best. The task of automatically selecting an algorithm from a given set is known as the per-instance algorithm selection problem and has been intensely studied over the past 15 years, leading to major improvements in the state of the art in solving a growing number of discrete combinatorial problems, including propositional satisfiability and AI planning. Perinstance algorithm selection also shows much promise for boosting performance in solving continuous and mixed discrete/continuous optimisation problems.This survey provides an overview of research in automated algorithm selection, ranging from early and seminal works to recent and promising application areas. Different from earlier work, it covers applications to discrete and continuous problems, and discusses algorithm selection in context with conceptually related approaches, such as algorithm configuration, scheduling or portfolio selection. Since informative and cheaply computable problem instance features provide the basis for effective perinstance algorithm selection systems, we also provide an overview of such features for discrete and continuous problems. Finally, we provide perspectives on future work in the area and discuss a number of open research challenges.

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Section: Planzillamentioning

confidence: 99%

Section: Planzillamentioning

confidence: 99%

Automated Algorithm Selection: Survey and Perspectives

Kerschke

Hoos

Neumann

et al. 2019

Evolutionary Computation

Self Cite

317

184

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show abstract

“…SATZilla (Xu et al, 2012) has been rather influential also outside the SAT domain. For example, in AI planning, Planzilla (Rizzini, Fawcett, Vallati, Gerevini, & Hoos, 2017) and its improved variants (model-based approaches) were all inspired by the random forests and regression techniques proposed by SATZilla/Zilla. Similarly, for Satisfiability Modulo Theories (SMT) problems, MachSMT (Scott, Niemetz, Preiner, Nejati, & Ganesh, 2021) was recently introduced and its essential parts also rely on random forests.…”

Section: Related Workmentioning

confidence: 99%

sunny-as2: Enhancing SUNNY for Algorithm Selection

Liu¹,

Amadini

Gabbrielli

et al. 2021

jair

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SUNNY is an Algorithm Selection (AS) technique originally tailored for Constraint Programming (CP). SUNNY is based on the k-nearest neighbors algorithm and enables one to schedule, from a portfolio of solvers, a subset of solvers to be run on a given CP problem. This approach has proved to be effective for CP problems. In 2015, the ASlib benchmarks were released for comparing AS systems coming from disparate fields (e.g., ASP, QBF, and SAT) and SUNNY was extended to deal with generic AS problems. This led to the development of sunny-as, a prototypical algorithm selector based on SUNNY for ASlib scenarios. A major improvement of sunny-as, called sunny-as2, was then submitted to the Open Algorithm Selection Challenge (OASC) in 2017, where it turned out to be the best approach for the runtime minimization of decision problems. In this work we present the technical advancements of sunny-as2, by detailing through several empirical evaluations and by providing new insights. Its current version, built on the top of the preliminary version submitted to OASC, is able to outperform sunny-as and other state-of-the-art AS methods, including those who did not attend the challenge.

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“…AllPACA is a portfolio that selects the most promising optimal planner to run on a given planning task. A comparison of static and dynamic portfolio techniques, focused on optimal planning, has been recently done by Rizzini et al…”

Section: Related Workmentioning

confidence: 99%

On the predictability of domain‐independent temporal planners

Cenamor

Vallati

Chrpa

2019

Computational Intelligence

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Temporal planning is a research discipline that addresses the problem of generating a totally or a partially ordered sequence of actions that transform the environment from some initial state to a desired goal state, while taking into account time constraints and actions' duration. For its ability to describe and address temporal constraints, temporal planning is of critical importance for a wide range of real‐world applications. Predicting the performance of temporal planners can lead to significant improvements in the area, as planners can then be combined in order to boost the performance on a given set of problem instances. This paper investigates the predictability of the state‐of‐the‐art temporal planners by introducing a new set of temporal‐specific features and exploiting them for generating classification and regression empirical performance models (EPMs) of considered planners. EPMs are also tested with regard to their ability to select the most promising planner for efficiently solving a given temporal planning problem. Our extensive empirical analysis indicates that the introduced set of features allows to generate EPMs that can effectively perform algorithm selection, and the use of EPMs is therefore a promising direction for improving the state of the art of temporal planning, hence fostering the use of planning in real‐world applications.

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Static and Dynamic Portfolio Methods for Optimal Planning: An Empirical Analysis

Cited by 13 publications

References 18 publications

Automated Algorithm Selection: Survey and Perspectives

Automated Algorithm Selection: Survey and Perspectives

sunny-as2: Enhancing SUNNY for Algorithm Selection

On the predictability of domain‐independent temporal planners

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