Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/5244
Título: Adaptive learning in agents behaviour: A framework for electricity markets simulation
Autor: Pinto, Tiago
Vale, Zita
Sousa, Tiago
Praça, Isabel
Santos, Gabriel
Morais, Hugo
Palavras-chave: Adaptive learning
Artificial Intelligence
Electricity markets
Machine learning
Multiagent simulation
Data: 2014
Editora: IOS Press
Relatório da Série N.º: Integrated Computer-Aided Engineering;Vol.21, nº 4
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.
Peer review: yes
URI: http://hdl.handle.net/10400.22/5244
DOI: 10.3233/ICA-140477
Versão do Editor: http://iospress.metapress.com/content/x2282034073838q3/?p=632a8354ab3441fa88ab2782e81846bc&pi=6
Aparece nas colecções:ISEP – GECAD – Artigos

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