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Algorithm Selection for Combinatorial Search Problems: A Survey
, 2012
"... The Algorithm Selection Problem is concerned with selecting the best algorithm to solve a given problem on a casebycase basis. It has become especially relevant in the last decade, as researchers are increasingly investigating how to identify the most suitable existing algorithm for solving a prob ..."
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The Algorithm Selection Problem is concerned with selecting the best algorithm to solve a given problem on a casebycase basis. It has become especially relevant in the last decade, as researchers are increasingly investigating how to identify the most suitable existing algorithm for solving a problem instead of developing new algorithms. This survey presents an overview of this work focusing on the contributions made in the area of combinatorial search problems, where Algorithm Selection techniques have achieved significant performance improvements. We unify and organise the vast literature according to criteria that determine Algorithm Selection systems in practice. The comprehensive classification of approaches identifies and analyses the different directions from which Algorithm Selection has been approached. This paper contrasts and compares different methods for solving the problem as well as ways of using these solutions. It closes by identifying directions of current and future research.
LIBACOP and LIBACOP2 Planner
"... This document describes two planning portfolios developed for the Learning Track of IPC2014 (International Planning Competition). The Learning InstanceBased Configured Portfolios are based on predictive models learnt with training instances gathered from previous executions of the base planners. ..."
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This document describes two planning portfolios developed for the Learning Track of IPC2014 (International Planning Competition). The Learning InstanceBased Configured Portfolios are based on predictive models learnt with training instances gathered from previous executions of the base planners. For solving new planning problems, the portfolios select a group of planners using the predictions of the learnt models. Specifically, LIBACOP uses a classification model to select potential planners assigning the same execution time to each of them. LIBACOP2 uses the same classification model and, in addition, a regression model to predict the time for each planner.
MacroSatPlan: Combining macros and SAT planning
"... Abstract Planning based on propositional satisfiability is a powerful approach for computing makespanoptimal plans. However, it is usually slower then heuristicbased suboptimal approaches. In this work we propose MacroSatPlan; a SatPlan based planner which exploits macros extracted by MacroFF an ..."
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Abstract Planning based on propositional satisfiability is a powerful approach for computing makespanoptimal plans. However, it is usually slower then heuristicbased suboptimal approaches. In this work we propose MacroSatPlan; a SatPlan based planner which exploits macros extracted by MacroFF and uses a predictive model of the optimal solution length that is constructed by WEKA, a commonly used toolkit of machine learning algorithms. First we briefly present the SatPlan approach. Then we describe the architecture of MacroSatPlan. Finally we present the results of an experimental study evaluating MacroSatPlan.
Algorithm Selection for Search: A survey Algorithm Selection for Combinatorial Search Problems: A survey
"... Abstract The Algorithm Selection Problem is concerned with selecting the best algorithm to solve a given problem on a casebycase basis. It has become especially relevant in the last decade, as researchers are increasingly investigating how to identify the most suitable existing algorithm for solv ..."
Abstract
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Abstract The Algorithm Selection Problem is concerned with selecting the best algorithm to solve a given problem on a casebycase basis. It has become especially relevant in the last decade, as researchers are increasingly investigating how to identify the most suitable existing algorithm for solving a problem instead of developing new algorithms. This survey presents an overview of this work focusing on the contributions made in the area of combinatorial search problems, where Algorithm Selection techniques have achieved significant performance improvements. We unify and organise the vast literature according to criteria that determine Algorithm Selection systems in practice. The comprehensive classification of approaches identifies and analyses the different directions from which Algorithm Selection has been approached. This paper contrasts and compares different methods for solving the problem as well as ways of using these solutions. It closes by identifying directions of current and future research.