On the Evolution of Planner-Specific Macro Sets

Mauro Vallati, Lukas Chrpa, Ivan Serina

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)


In Automated Planning, generating macro-operators (macros) is a well-known reformulation approach that is used to speed-up the planning process. Most of the macro generation techniques aim for using the same set of generated macros on every problem instance of a given domain. This limits the usefulness of macros in scenarios where the environment and thus the structure of instances is dynamic, such as in real-world applications. Moreover, despite the wide availability of parallel processing units, there is a lack of approaches that can take advantage of multiple parallel cores, while exploiting macros.

In this paper we propose the Macro sets Evolution (MEvo) approach. MEvo has been designed for overcoming the aforementioned issues by exploiting multiple cores for combining promising macros –taken from a given pool– in different sets, while solving continuous streams of problem instances. Our empirical study, involving 5 state-of-the-art planning engines and a large number of planning instances, demonstrates the effectiveness of the proposed MEvo approach.
Original languageEnglish
Title of host publicationAI*AI 2017 Advances in Artificial Intelligence
Subtitle of host publicationXVIth International Conference of the Italian Association for Artificial Intelligence, Bari, Italy, November 14-17, 2017, Proceedings
EditorsFloriana Esposito, Roberto Basili, Stefano Ferilli, Francesca A. Lisi
PublisherSpringer Verlag
Number of pages12
ISBN (Electronic)9783319701691
ISBN (Print)9783319701691
Publication statusPublished - 7 Nov 2017
Event16th International Conference of the Italian Association for Artificial Intelligence - University of Bari, Bari, Italy
Duration: 14 Nov 201717 Nov 2017
Conference number: 16
http://aiia2017.di.uniba.it/ (Link to Conference Website)

Publication series

NameLecture Notes in Artificial Intelligence (LNAI)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th International Conference of the Italian Association for Artificial Intelligence
Abbreviated titleAI*IA 2017
Internet address


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