Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/5932
Título: Adaptive Learning in Games: Defining Profiles of Competitor Players
Autor: Pinto, Tiago
Vale, Zita
Palavras-chave: Artificial Intelligence
Adaptive Learning
Player Profiles
Data: 2013
Editora: Springer
Relatório da Série N.º: Advances in Intelligent Systems and Computing;Vol. 217
Resumo: Artificial Intelligence has been applied to dynamic games for many years. The ultimate goal is creating responses in virtual entities that display human-like reasoning in the definition of their behaviors. However, virtual entities that can be mistaken for real persons are yet very far from being fully achieved. This paper presents an adaptive learning based methodology for the definition of players’ profiles, with the purpose of supporting decisions of virtual entities. The proposed methodology is based on reinforcement learning algorithms, which are responsible for choosing, along the time, with the gathering of experience, the most appropriate from a set of different learning approaches. These learning approaches have very distinct natures, from mathematical to artificial intelligence and data analysis methodologies, so that the methodology is prepared for very distinct situations. This way it is equipped with a variety of tools that individually can be useful for each encountered situation. The proposed methodology is tested firstly on two simpler computer versus human player games: the rock-paper-scissors game, and a penalty-shootout simulation. Finally, the methodology is applied to the definition of action profiles of electricity market players; players that compete in a dynamic game-wise environment, in which the main goal is the achievement of the highest possible profits in the market.
Peer review: yes
URI: http://hdl.handle.net/10400.22/5932
DOI: 10.1007/978-3-319-00551-5_43
Versão do Editor: http://link.springer.com/chapter/10.1007/978-3-319-00551-5_43
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