Por favor, use este identificador para citar o enlazar a este item:
http://hdl.handle.net/10261/132911
COMPARTIR / EXPORTAR:
SHARE CORE BASE | |
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE | |
Título: | Efficient interactive decision-making framework for robotic applications |
Autor: | Agostini, Alejandro CSIC ORCID; Torras, Carme CSIC ORCID ; Wörgötter, Florentin | Palabras clave: | Decision making Human-like task Logic-based planning Online learning Robotics |
Fecha de publicación: | 2017 | Editor: | Elsevier | Citación: | Artificial Intelligence: 187-212 (2017) | Resumen: | The inclusion of robots in our society is imminent, such as service robots. Robots are now capable of reliably manipulating objects in our daily lives but only when combined with artificial intelligence (AI) techniques for planning and decision-making, which allow a machine to determine how a task can be completed successfully. To perform decision making, AI planning methods use a set of planning operators to code the state changes in the environment produced by a robotic action. Given a specific goal, the planner then searches for the best sequence of planning operators, i.e., the best plan that leads through the state space to satisfy the goal. In principle, planning operators can be hand-coded, but this is impractical for applications that involve many possible state transitions. An alternative is to learn them automatically from experience, which is most efficient when there is a human teacher. In this study, we propose a simple and efficient decision-making framework for this purpose. The robot executes its plan in a step-wise manner and any planning impasse produced by missing operators is resolved online by asking a human teacher for the next action to execute. Based on the observed state transitions, this approach rapidly generates the missing operators by evaluating the relevance of several cause–effect alternatives in parallel using a probability estimate, which compensates for the high uncertainty that is inherent when learning from a small number of samples. We evaluated the validity of our approach in simulated and real environments, where it was benchmarked against previous methods. Humans learn in the same incremental manner, so we consider that our approach may be a better alternative to existing learning paradigms, which require offline learning, a significant amount of previous knowledge, or a large number of samples. | Versión del editor: | http://dx.doi.org/10.1016/j.artint.2015.04.004 | URI: | http://hdl.handle.net/10261/132911 | DOI: | 10.1016/j.artint.2015.04.004 | ISSN: | 0004-3702 | E-ISSN: | 1872-7921 |
Aparece en las colecciones: | (IRII) Artículos |
Ficheros en este ítem:
Fichero | Descripción | Tamaño | Formato | |
---|---|---|---|---|
Robotic-Applications.pdf | 1,54 MB | Adobe PDF | Visualizar/Abrir |
CORE Recommender
SCOPUSTM
Citations
24
checked on 12-abr-2024
WEB OF SCIENCETM
Citations
16
checked on 29-feb-2024
Page view(s)
224
checked on 15-abr-2024
Download(s)
208
checked on 15-abr-2024
Google ScholarTM
Check
Altmetric
Altmetric
NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.