DOVETAIL - An abstraction for classical planning using a visual metaphor

Mauricio Cecflio Magnaguagno, Ramon Fraga Pereira, Felipe Meneguzzi

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

2 Citations (Scopus)

Abstract

While domain descriptions are often shared and manipulated through diagrams, most complex domains are still described using text-based languages. Code becomes an intermediary between the real-world and an abstract idea, and the programmer is merely a converter of diagrams into code. For automated planning this is no different. The state transition function is described in terms of a textual representation of actions and, although simple actions require little effort to define by the user, the learning process is often slow. New users have no metaphor to help them to visualize the domain description that they are working on and little information about why a planner fails due to formalization errors. In this paper, we propose a visual abstraction for both the planning domain actions and the planning process itself, to facilitate the design of classical planning domains. Using this abstraction, we expect to improve the learning curve for defining and subsequently diagnosing problems with new planning domains.

Original languageEnglish
Title of host publicationProceedings of the 29th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2016
EditorsZdravko Markov, Ingrid Russell
PublisherAAAI Press
Pages74-79
Number of pages6
ISBN (Electronic)9781577357568
Publication statusPublished - 2016
Event29th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2016 - Key Largo, United States
Duration: 16 May 201618 May 2016

Publication series

NameProceedings of the 29th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2016

Conference

Conference29th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2016
Country/TerritoryUnited States
CityKey Largo
Period16/05/1618/05/16

Bibliographical note

Publisher Copyright:
© 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

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