MEvo: A Framework for Effective Macro Sets Evolution

Mauro Vallati, Lukáš Chrpa, Ivan Serina

Research output: Contribution to journalArticle

Abstract

In Automated Planning, generating macro-operators (macros) is a well-known reformulation approach that is used to speed-up the planning process. Nowadays, given the number of existing techniques, a large number of macros is already available or can be easily extracted. Most of the macro generation techniques aim for using the same set of generated macros for each planner and every problem instance in a given domain. Although they provide “general improvement”, the effect of macros might vary a lot for different planners. Moreover, the impact of macros on structurally different problem instances than the training ones can be potentially very detrimental. Evidently, this limits the exploitation of macros in real-world planning applications, where the structure of problem instances can often change as well as the exploited planning engine can change from time to time. In this paper we propose the Macro sets Evolution (MEvo) approach. MEvo has been designed for overcoming the aforementioned issues in order to improve the performance of domain-independent planners by dynamically selecting promising macros –taken from a given pool– while solving continuous streams of problem instances. Our extensive empirical study, involving more than 1,000 planning problem instances and 8 state-of-the-art planning engines, demonstrates effectiveness and efficiency of MEvo.
LanguageEnglish
JournalJournal of Experimental and Theoretical Artificial Intelligence
Publication statusAccepted/In press - 10 Aug 2019

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Macros
Planning
Engine
Process Planning
Reformulation
Exploitation
Empirical Study
Speedup
Framework
Vary
Engines
Operator
Demonstrate

Cite this

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title = "MEvo: A Framework for Effective Macro Sets Evolution",
abstract = "In Automated Planning, generating macro-operators (macros) is a well-known reformulation approach that is used to speed-up the planning process. Nowadays, given the number of existing techniques, a large number of macros is already available or can be easily extracted. Most of the macro generation techniques aim for using the same set of generated macros for each planner and every problem instance in a given domain. Although they provide “general improvement”, the effect of macros might vary a lot for different planners. Moreover, the impact of macros on structurally different problem instances than the training ones can be potentially very detrimental. Evidently, this limits the exploitation of macros in real-world planning applications, where the structure of problem instances can often change as well as the exploited planning engine can change from time to time. In this paper we propose the Macro sets Evolution (MEvo) approach. MEvo has been designed for overcoming the aforementioned issues in order to improve the performance of domain-independent planners by dynamically selecting promising macros –taken from a given pool– while solving continuous streams of problem instances. Our extensive empirical study, involving more than 1,000 planning problem instances and 8 state-of-the-art planning engines, demonstrates effectiveness and efficiency of MEvo.",
keywords = "Automated Planning, Domain Model Reformation, Sets Evolution",
author = "Mauro Vallati and Luk{\'a}š Chrpa and Ivan Serina",
year = "2019",
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language = "English",
journal = "Journal of Experimental and Theoretical Artificial Intelligence",
issn = "0952-813X",
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MEvo : A Framework for Effective Macro Sets Evolution. / Vallati, Mauro; Chrpa, Lukáš; Serina, Ivan.

In: Journal of Experimental and Theoretical Artificial Intelligence, 10.08.2019.

Research output: Contribution to journalArticle

TY - JOUR

T1 - MEvo

T2 - Journal of Experimental and Theoretical Artificial Intelligence

AU - Vallati, Mauro

AU - Chrpa, Lukáš

AU - Serina, Ivan

PY - 2019/8/10

Y1 - 2019/8/10

N2 - In Automated Planning, generating macro-operators (macros) is a well-known reformulation approach that is used to speed-up the planning process. Nowadays, given the number of existing techniques, a large number of macros is already available or can be easily extracted. Most of the macro generation techniques aim for using the same set of generated macros for each planner and every problem instance in a given domain. Although they provide “general improvement”, the effect of macros might vary a lot for different planners. Moreover, the impact of macros on structurally different problem instances than the training ones can be potentially very detrimental. Evidently, this limits the exploitation of macros in real-world planning applications, where the structure of problem instances can often change as well as the exploited planning engine can change from time to time. In this paper we propose the Macro sets Evolution (MEvo) approach. MEvo has been designed for overcoming the aforementioned issues in order to improve the performance of domain-independent planners by dynamically selecting promising macros –taken from a given pool– while solving continuous streams of problem instances. Our extensive empirical study, involving more than 1,000 planning problem instances and 8 state-of-the-art planning engines, demonstrates effectiveness and efficiency of MEvo.

AB - In Automated Planning, generating macro-operators (macros) is a well-known reformulation approach that is used to speed-up the planning process. Nowadays, given the number of existing techniques, a large number of macros is already available or can be easily extracted. Most of the macro generation techniques aim for using the same set of generated macros for each planner and every problem instance in a given domain. Although they provide “general improvement”, the effect of macros might vary a lot for different planners. Moreover, the impact of macros on structurally different problem instances than the training ones can be potentially very detrimental. Evidently, this limits the exploitation of macros in real-world planning applications, where the structure of problem instances can often change as well as the exploited planning engine can change from time to time. In this paper we propose the Macro sets Evolution (MEvo) approach. MEvo has been designed for overcoming the aforementioned issues in order to improve the performance of domain-independent planners by dynamically selecting promising macros –taken from a given pool– while solving continuous streams of problem instances. Our extensive empirical study, involving more than 1,000 planning problem instances and 8 state-of-the-art planning engines, demonstrates effectiveness and efficiency of MEvo.

KW - Automated Planning

KW - Domain Model Reformation

KW - Sets Evolution

M3 - Article

JO - Journal of Experimental and Theoretical Artificial Intelligence

JF - Journal of Experimental and Theoretical Artificial Intelligence

SN - 0952-813X

ER -