Adaptive learning in agents behaviour: A framework for electricity markets simulation

Detalhes bibliográficos
Autor(a) principal: Pinto, Tiago
Data de Publicação: 2014
Outros Autores: Vale, Zita, Sousa, Tiago, Praça, Isabel, Santos, Gabriel, Morais, Hugo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.22/5244
Resumo: Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.
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spelling Adaptive learning in agents behaviour: A framework for electricity markets simulationAdaptive learningArtificial IntelligenceElectricity marketsMachine learningMultiagent simulationElectricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.IOS PressRepositório Científico do Instituto Politécnico do PortoPinto, TiagoVale, ZitaSousa, TiagoPraça, IsabelSantos, GabrielMorais, Hugo2014-12-09T13:01:59Z20142014-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/5244eng10.3233/ICA-140477info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-13T12:45:16Zoai:recipp.ipp.pt:10400.22/5244Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:25:56.681513Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Adaptive learning in agents behaviour: A framework for electricity markets simulation
title Adaptive learning in agents behaviour: A framework for electricity markets simulation
spellingShingle Adaptive learning in agents behaviour: A framework for electricity markets simulation
Pinto, Tiago
Adaptive learning
Artificial Intelligence
Electricity markets
Machine learning
Multiagent simulation
title_short Adaptive learning in agents behaviour: A framework for electricity markets simulation
title_full Adaptive learning in agents behaviour: A framework for electricity markets simulation
title_fullStr Adaptive learning in agents behaviour: A framework for electricity markets simulation
title_full_unstemmed Adaptive learning in agents behaviour: A framework for electricity markets simulation
title_sort Adaptive learning in agents behaviour: A framework for electricity markets simulation
author Pinto, Tiago
author_facet Pinto, Tiago
Vale, Zita
Sousa, Tiago
Praça, Isabel
Santos, Gabriel
Morais, Hugo
author_role author
author2 Vale, Zita
Sousa, Tiago
Praça, Isabel
Santos, Gabriel
Morais, Hugo
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Repositório Científico do Instituto Politécnico do Porto
dc.contributor.author.fl_str_mv Pinto, Tiago
Vale, Zita
Sousa, Tiago
Praça, Isabel
Santos, Gabriel
Morais, Hugo
dc.subject.por.fl_str_mv Adaptive learning
Artificial Intelligence
Electricity markets
Machine learning
Multiagent simulation
topic Adaptive learning
Artificial Intelligence
Electricity markets
Machine learning
Multiagent simulation
description Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.
publishDate 2014
dc.date.none.fl_str_mv 2014-12-09T13:01:59Z
2014
2014-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.22/5244
url http://hdl.handle.net/10400.22/5244
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.3233/ICA-140477
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dc.publisher.none.fl_str_mv IOS Press
publisher.none.fl_str_mv IOS Press
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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