A Guide to Portfolio-Based Planning

Vallati, Mauro

doi:10.1007/978-3-642-35455-7_6

Cited by 7 publications

(6 citation statements)

References 18 publications

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“…Since planning is intractable in general (Chapman 1987) and even classical planning is PSPACE-complete (Bylander 1994), a single algorithm unlikely works well for all problem domains. Hence, surging interest exists in developing portfolio-based approaches (Seipp et al 2012;Vallati 2012;Howe et al 1999;Seipp et al 2015), which, for a set of planners, compute an offline schedule or an online decision regarding which planner to invoke per planning task. While offline portfolio approaches focus on finding a single invocation schedule that is expected to work well across all planning tasks, online methods learn to choose the right planner for each given task.…”

Section: Introductionmentioning

confidence: 99%

Online Planner Selection with Graph Neural Networks and Adaptive Scheduling

Ma¹,

Ferber

Huo³

et al. 2020

AAAI

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Automated planning is one of the foundational areas of AI. Since no single planner can work well for all tasks and domains, portfolio-based techniques have become increasingly popular in recent years. In particular, deep learning emerges as a promising methodology for online planner selection. Owing to the recent development of structural graph representations of planning tasks, we propose a graph neural network (GNN) approach to selecting candidate planners. GNNs are advantageous over a straightforward alternative, the convolutional neural networks, in that they are invariant to node permutations and that they incorporate node labels for better inference.Additionally, for cost-optimal planning, we propose a two-stage adaptive scheduling method to further improve the likelihood that a given task is solved in time. The scheduler may switch at halftime to a different planner, conditioned on the observed performance of the first one. Experimental results validate the effectiveness of the proposed method against strong baselines, both deep learning and non-deep learning based.The code is available at https://github.com/matenure/GNN_planner.

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Section: Introductionmentioning

confidence: 99%

Online Planner Selection with Graph Neural Networks and Adaptive Scheduling

Ma¹,

Ferber

Huo³

et al. 2020

AAAI

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“…One such approach is to create portfolios of planners to leverage their combined strengths (Seipp et al 2012;Vallati 2012;Cenamor, de la Rosa, and Fernández 2013;Seipp et al 2015). Besides parallel portfolios that are wellsuited to exploit multiple CPUs, many portfolios are sequential, which means that they execute one or more planners sequentially on a given task.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Cost-Optimal Planning: Task-Dependent Planner Selection

Sievers

Katz²,

Sohrabi³

et al. 2019

AAAI

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As classical planning is known to be computationally hard, no single planner is expected to work well across many planning domains. One solution to this problem is to use online portfolio planners that select a planner for a given task. These portfolios perform a classification task, a well-known and wellresearched task in the field of machine learning. The classification is usually performed using a representation of planning tasks with a collection of hand-crafted statistical features. Recent techniques in machine learning that are based on automatic extraction of features have not been employed yet due to the lack of suitable representations of planning tasks.In this work, we alleviate this barrier. We suggest representing planning tasks by images, allowing to exploit arguably one of the most commonly used and best developed techniques in deep learning. We explore some of the questions that inevitably rise when applying such a technique, and present various ways of building practically useful online portfoliobased planners. An evidence of the usefulness of our proposed technique is a planner that won the cost-optimal track of the International Planning Competition 2018.

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“…and (3) in which order. There are many techniques to configure a planning portfolio (Vallati, 2012), and depending on how accurate they are, the chances of selecting the best planner in a given situation will increase. Note that, in this definition, if a planner has different configuration parameters which modify its behavior, each parameterization is considered a different base planner, so base planners can be considered as black boxes.…”

Section: Introductionmentioning

confidence: 99%

The IBaCoP Planning System: Instance-Based Configured Portfolios

Cenamor¹,

Rosa²,

Fernández³

2016

jair

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Sequential planning portfolios are very powerful in exploiting the complementary strength of different automated planners. The main challenge of a portfolio planner is to define which base planners to run, to assign the running time for each planner and to decide in what order they should be carried out to optimize a planning metric. Portfolio configurations are usually derived empirically from training benchmarks and remain fixed for an evaluation phase. In this work, we create a per-instance configurable portfolio, which is able to adapt itself to every planning task. The proposed system pre-selects a group of candidate planners using a Pareto-dominance filtering approach and then it decides which planners to include and the time assigned according to predictive models. These models estimate whether a base planner will be able to solve the given problem and, if so, how long it will take. We define different portfolio strategies to combine the knowledge generated by the models. The experimental evaluation shows that the resulting portfolios provide an improvement when compared with non-informed strategies. One of the proposed portfolios was the winner of the Sequential Satisficing Track of the International Planning Competition held in 2014.

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A Guide to Portfolio-Based Planning

Cited by 7 publications

References 18 publications

Online Planner Selection with Graph Neural Networks and Adaptive Scheduling

Online Planner Selection with Graph Neural Networks and Adaptive Scheduling

Deep Learning for Cost-Optimal Planning: Task-Dependent Planner Selection

The IBaCoP Planning System: Instance-Based Configured Portfolios

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