Article Dans Une Revue Knowledge Engineering Review Année : 2018

A Review of Learning Planning Action Models

Résumé

Automated planning has been a continuous field of study since the 1960s, since the notion of accomplishing a task using an ordered set of actions resonates with almost every known activity domain. However, as we move from toy domains closer to the complex real world, these actions become increasingly difficult to codify. The reasons range from intense laborious effort, to intricacies so barely identifiable, that programming them is a challenge that presents itself much later in the process. In such domains, planners now leverage recent advancements in machine learning to learn action models i.e. blueprints of all the actions whose execution effectuates transitions in the system. This learning provides an opportunity for the evolution of the model towards a version more consistent and adapted to its environment, augmenting the probability of success of the plans. It is also a conscious effort to decrease laborious manual coding and increase quality. This paper presents a survey of the machine learning techniques applied for learning planning action models. It first describes the characteristics of learning systems. It then details the learning techniques that have been used in the literature during the past decades, and finally presents some open issues.

Fichier principal
Vignette du fichier
A_Review_of_Learning_Planning_Action_Models.pdf (834.41 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Licence
Loading...

Dates et versions

hal-02010536 , version 1 (07-02-2019)

Licence

Identifiants

Citer

Ankuj Arora, Humbert Fiorino, Damien Pellier, Marc M ´ Etivier, Sylvie Pesty. A Review of Learning Planning Action Models. Knowledge Engineering Review, 2018, 33, ⟨10.1017/S0269888918000188⟩. ⟨hal-02010536⟩
282 Consultations
2833 Téléchargements

Altmetric

Partager

  • More