ERA: Extracting planning macro-operators from adjacent and non-adjacent sequences - Université Grenoble Alpes Accéder directement au contenu
Communication Dans Un Congrès Année : 2021

ERA: Extracting planning macro-operators from adjacent and non-adjacent sequences

Résumé

Intuitively, Automated Planning systems capable of learning from previous experiences should be able to achieve better performance. One way to build on past experiences is to augment domains with macro-operators (i.e. frequent operator sequences). In most existing works, macros are generated from chunks of adjacent operators extracted from a set of plans. Although they provide some interesting results this type of analysis may provide incomplete results. In this paper, we propose ERA, an automatic extraction method for macro-operators from a set of solution plans. Our algorithm is domain and planner independent and can fi nd all macro-operator occurrences even if the operators are non-adjacent. Our method has proven to successfully find macrooperators of di erent lengths for six diff erent benchmark domains. Also, our experiments highlighted the capital role of considering non-adjacent occurrences in the extraction of macro-operators.
Fichier principal
Vignette du fichier
PKAW_ERA_20.pdf (1.1 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03131334 , version 1 (04-02-2021)

Identifiants

  • HAL Id : hal-03131334 , version 1

Citer

Sandra Castellanos-Paez, Romain Rombourg, Philippe Lalanda. ERA: Extracting planning macro-operators from adjacent and non-adjacent sequences. 2020 Principle and Practice of Data and Knowledge Acquisition Workshop, Jan 2021, Yokohama, Japan. ⟨hal-03131334⟩
57 Consultations
131 Téléchargements

Partager

Gmail Facebook X LinkedIn More