An Automatic Algorithm Selection Approach for Planning

Vallati, Mauro; Chrpa, Luka; Kitchin, Diane E.

doi:10.1109/ictai.2013.12

Cited by 5 publications

(9 citation statements)

References 17 publications

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“…Other directions for further research are investigating the use of PbP.s/q for optimal planning and for metric-temporal domains , and extending the portfolios with additional automatically extracted domain-specific knowledge, such as entanglements (Vallati et al, 2013a). Finally, we intend to investigate the idea of making PbP fully domainindependent by computing many portfolio configurations (planner clusters) for different known domains, and using a classifier to match a new domain with the most promising stored configuration in terms of expected performance for the new domain.…”

Section: Discussionmentioning

confidence: 99%

“…ASAP (Vallati, Chrpa, & Kitchin, 2013a) is a recent system for selecting the most promising planner from a set of candidates planners that derives much of its power from the use of entanglements (Chrpa & Barták, 2009;. Entanglements are relations between planning operators and predicates used to reformulate the domain model by removing unpromising operator instances or restricting the applicability of some actions to certain states.…”

Section: Planner Portfolio Design In Automated Planningmentioning

confidence: 99%

“…Consequently, as stated by Gibbons and Chakraborti (2003), it is critical that we use a non-parameterized test, such as the Wilcoxon sign-rank test. We have also investigated the usage of other methods to compare the performance of the simulated runs of planner clusters, including the IPC score function that was also used by Vallati et al (2013a). However, we experimentally observed that, for the IPC7 domains, such a method is less effective than the usage of the Wilcoxon sign-rank test.…”

Section: Selecting a Planner Clustermentioning

confidence: 99%

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Planning through Automatic Portfolio Configuration: The PbP Approach

Gerevini¹,

Saetti²,

Vallati³

2014

jair

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In the field of domain-independent planning, several powerful planners implementing different techniques have been developed. However, no one of these systems outperforms all others in every known benchmark domain. In this work, we propose a multi-planner approach that automatically configures a portfolio of planning techniques for each given domain. The configuration process for a given domain uses a set of training instances to: (i) compute and analyze some alternative sets of macro-actions for each planner in the portfolio identifying a (possibly empty) useful set, (ii) select a cluster of planners, each one with the identified useful set of macro-actions, that is expected to perform best, and (iii) derive some additional information for configuring the execution scheduling of the selected planners at planning time. The resulting planning system, called PbP (Portfolio- based Planner), has two variants focusing on speed and plan quality. Different versions of PbP entered and won the learning track of the sixth and seventh International Planning Competitions. In this paper, we experimentally analyze PbP considering planning speed and plan quality in depth. We provide a collection of results that help to understand PbPs behavior, and demonstrate the effectiveness of our approach to configuring a portfolio of planners with macro-actions.

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Section: Discussionmentioning

confidence: 99%

Section: Planner Portfolio Design In Automated Planningmentioning

confidence: 99%

Section: Selecting a Planner Clustermentioning

confidence: 99%

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Planning through Automatic Portfolio Configuration: The PbP Approach

Gerevini¹,

Saetti²,

Vallati³

2014

jair

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“…PBP was the overall winner of the learning track of the 6th International Planning Competition, and PBP2 brought further improvement through the integration of automated algorithm configuration to better exploit the performance potential of parameterised planners. The ASAP planning system is based on similar ideas (Vallati et al, 2013. In addition to macro-actions, ASAP also exploits so-called entanglements, which reflect causal relationships that are characteristic for a given domain of planning problems.…”

Section: Per-domain Selection Approachesmentioning

confidence: 99%

Automated Algorithm Selection: Survey and Perspectives

Kerschke

Hoos

Neumann

et al. 2019

Evolutionary Computation

318

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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“…An automatic Algorithm Selection Approach for Planning (ASAP) [58] is the only approach based on pure automatic algorithm selection. For a given domain, ASAP learns additional knowledge, in the form of macro-operators and entanglements [8], which is used for creating different encodings of the given planning domain and problems (i.e.…”

Section: Asapmentioning

confidence: 99%

Portfolio-based planning: State of the art, common practice and open challenges

Vallati

Chrpa

Kitchin

2015

AIC

Self Cite

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In recent years the field of automated planning has significantly advanced and several powerful domain-independent planners have been developed. However, none of these systems clearly outperforms all the others in every known benchmark domain. This observation motivated the idea of configuring and exploiting a portfolio of planners to perform better than any individual planner: some recent planning systems based on this idea achieved significantly good results in experimental analysis and International Planning Competitions. Such results let us suppose that future challenges of the Automated Planning community will converge on designing different approaches for combining existing planning algorithms. This paper reviews existing techniques and provides an exhaustive guide to portfolio-based planning. In addition, the paper outlines open issues of existing approaches and highlights possible future evolution of these techniques.

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An Automatic Algorithm Selection Approach for Planning

Cited by 5 publications

References 17 publications

Planning through Automatic Portfolio Configuration: The PbP Approach

Planning through Automatic Portfolio Configuration: The PbP Approach

Automated Algorithm Selection: Survey and Perspectives

Portfolio-based planning: State of the art, common practice and open challenges

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