Mostra el registre d'ítem simple
Learning weakly correlated cause-effects for gardening with a cognitive system
dc.contributor.author | Agostini, Alejandro Gabriel |
dc.contributor.author | Torras, Carme |
dc.contributor.author | Worgotter, Florentin |
dc.contributor.other | Institut de Robòtica i Informàtica Industrial |
dc.date.accessioned | 2015-06-03T17:45:40Z |
dc.date.available | 2015-06-03T17:45:40Z |
dc.date.created | 2014 |
dc.date.issued | 2014 |
dc.identifier.citation | Agostini, A.G.; Torras, C.; Worgotter, F. Learning weakly correlated cause-effects for gardening with a cognitive system. "Engineering applications of artificial intelligence", 2014, vol. 36, p. 178-194. |
dc.identifier.issn | 0952-1976 |
dc.identifier.uri | http://hdl.handle.net/2117/28169 |
dc.description.abstract | We propose a cognitive system that combines artificial intelligence techniques for planning and learning to execute tasks involving delayed and variable correlations between the actions executed and their expected effects. The system is applied to the task of controlling the growth of plants, where the evolution of the plant attributes strongly depends on different events taking place in the temporally distant past history of the plant. The main problem to tackle is how to efficiently detect these past events. This is very challenging since the inclusion of time could make the dimensionality of the search space extremely large and the collected training instances may only provide very limited information about the relevant combinations of events. To address this problem we propose a learning method that progressively identifies those events that are more likely to produce a sequence of changes under a plant treatment. Since the number of experiences is very limited compared to the size of the event space, we use a probabilistic estimate that takes into account the lack of experience to prevent biased estimations. Planning operators are generated from most accurately predicted sequences of changes. Planning and learning are integrated in a decision-making framework that operates without task interruptions by allowing a human gardener to instruct the treatments when the knowledge acquired so far is not enough to make a decision. |
dc.format.extent | 17 p. |
dc.language.iso | eng |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Robòtica |
dc.subject.other | artificial intelligence |
dc.subject.other | intelligent robots |
dc.subject.other | learning (artificial intelligence) |
dc.subject.other | service robots |
dc.subject.other | rule learning |
dc.subject.other | weakly-correlated cause-effects |
dc.subject.other | cognitive system |
dc.subject.other | robot gardener |
dc.title | Learning weakly correlated cause-effects for gardening with a cognitive system |
dc.type | Article |
dc.contributor.group | Universitat Politècnica de Catalunya. ROBiri - Grup de Robòtica de l'IRI |
dc.identifier.doi | 10.1016/j.engappai.2014.07.017 |
dc.description.peerreviewed | Peer Reviewed |
dc.subject.inspec | Classificació INSPEC::Automation::Robots::Intelligent robots |
dc.relation.publisherversion | http://www.sciencedirect.com/science/article/pii/S0952197614001857 |
dc.rights.access | Open Access |
local.identifier.drac | 15269966 |
dc.description.version | Postprint (author’s final draft) |
dc.relation.projectid | info:eu-repo/grantAgreement/EC/FP7/247947/EU/Gardening with a Cognitive System/GARNICS |
local.citation.author | Agostini, A.G.; Torras, C.; Worgotter, F. |
local.citation.publicationName | Engineering applications of artificial intelligence |
local.citation.volume | 36 |
local.citation.startingPage | 178 |
local.citation.endingPage | 194 |
Fitxers d'aquest items
Aquest ítem apareix a les col·leccions següents
-
Articles de revista [163]
-
Articles de revista [376]