Learning and adaptation in physical heterogeneous teams of robots
dc.contributor.author
dc.date.accessioned
2009-11-23T16:32:30Z
dc.date.available
2009-11-23T16:32:30Z
dc.date.issued
2007
dc.identifier.issn
1888-0258
dc.identifier.uri
dc.description.abstract
In this paper we present a novel approach to assigning roles to robots in a team of physical heterogeneous robots. Its members compete for these roles and get rewards for them. The rewards are used to determine each agent’s preferences and which agents are better adapted to the environment. These aspects are included in the decision making process. Agent interactions are modelled using the concept of an ecosystem in which each robot is a species, resulting in emergent behaviour of the whole set of agents. One of the most important features of this approach is its high adaptability. Unlike some other learning techniques, this approach does not need to start a whole exploitation process when the environment changes. All this is exemplified by means of experiments run on a simulator. In addition, the algorithm developed was applied as applied to several teams of robots in order to analyse the impact of heterogeneity in these systems
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Red de Agentes Físicos
dc.relation.isformatof
Reproducció digital del document publicat a: http://www.jopha.net/index.php/jopha/article/view/8
dc.relation.ispartof
Journal of physical agents, 2007, vol. 1, núm. 1, p. 9-18
dc.relation.ispartofseries
Articles publicats (D-EEEiA)
dc.rights
Reconeixement-CompartirIgual 3.0 Espanya
dc.rights.uri
dc.subject
dc.title
Learning and adaptation in physical heterogeneous teams of robots
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.identifier.idgrec
008839