Repository logo
 
Publication

Adaptive learning in multiagent systems for automated energy contacts negotiation

2020Book Open access
Simple item page
dc.contributor.authorPinto, Tiago
dc.contributor.authorVale, Zita
dc.date.accessioned2021-09-17T15:13:45Z
dc.date.available2021-09-17T15:13:45Z
dc.date.issued2020
dc.description.abstractThis paper presents the Adaptive Decision Support for Electricity Markets Negotiations (AiD-EM) system. AiD-EM is a multi-agent system that provides decision support to market players by incorporating multiple sub-(agent-based) systems, directed to the decision support of specific problems. These sub-systems make use of different artificial intelligence methodologies, such as machine learning and evolutionary computation, to enable players adaptation in the planning phase and in actual negotiations in auction-based markets and bilateral negotiations.pt_PT
dc.description.sponsorshipThis work has been developed under the MAS-SOCIETY project - PTDC/EEI-EEE/28954/2017 and from FEDER Funds through COMPETE program and from National Funds through FCT under projects CEECIND/01811/2017 and UIDB/00760/2020pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3233/FAIA200458pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/18421
dc.language.isoengpt_PT
dc.publisherIOS Presspt_PT
dc.relation.publisherversionhttps://ebooks.iospress.nl/publication/55253pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectAdaptive Decision Support for Electricity Markets Negotiationspt_PT
dc.subjectAiD-EMpt_PT
dc.subjectAdaptative Learningpt_PT
dc.subjectEnergy Contractspt_PT
dc.subjectMulti-Agent Systemspt_PT
dc.titleAdaptive learning in multiagent systems for automated energy contacts negotiationpt_PT
dc.typebook
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/9471 - RIDTI/150159/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/CEEC IND 2017/4291/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/157466/PT
oaire.citation.endPage2930pt_PT
oaire.citation.startPage2929pt_PT
oaire.citation.titleEuropean Conference on Artificial Intelligence (ECAI 2020)pt_PT
oaire.fundingStream9471 - RIDTI
oaire.fundingStreamCEEC IND 2017
oaire.fundingStream6817 - DCRRNI ID
person.familyNamePinto
person.familyNameVale
person.givenNameTiago
person.givenNameZita
person.identifierR-000-T7J
person.identifier632184
person.identifier.ciencia-id2414-9B03-C4BB
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0001-8248-080X
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridT-2245-2018
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id35219107600
person.identifier.scopus-author-id7004115775
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typebookpt_PT
relation.isAuthorOfPublication8d58ddc0-1023-47c0-a005-129d412ce98d
relation.isAuthorOfPublicationff1df02d-0c0f-4db1-bf7d-78863a99420b
relation.isAuthorOfPublication.latestForDiscoveryff1df02d-0c0f-4db1-bf7d-78863a99420b
relation.isProjectOfPublication3ed6208b-ca2d-4045-acd4-71adf2095c38
relation.isProjectOfPublication9375f9c7-67c9-41bf-9916-7238351f1a56
relation.isProjectOfPublicationc8b89528-2f4d-4be2-b8bb-46826a29deeb
relation.isProjectOfPublication.latestForDiscoveryc8b89528-2f4d-4be2-b8bb-46826a29deeb

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
ART_GECAD_FAIA_2020.pdf
Size:
189.71 KB
Format:
Adobe Portable Document Format
Download

Collections

ISEP – GECAD – Artigos