Dinis Felix | Faculdade de Engenharia da Universidade do Porto (original) (raw)

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Research paper thumbnail of Computer Poker Research at LIACC

Cornell University - arXiv, Jan 24, 2013

Computer suited challenge for research in artificial intelligence. For that reason, and due to th... more Computer suited challenge for research in artificial intelligence. For that reason, and due to the Poker popularity in Portugal since 2008, several member of LIACC have researched in this field. Several works were published as papers and master theses and more recently a member of LIACC engaged on a research in this area as a Ph.D. thesis in order to develop a more extensive and in-depth work. This paper describes the existing research in LIACC about Computer Poker, with special emphasis on the completed master's theses and plans for future work. This paper means to present a summary of community in order to encourage the exchange of ideas with other labs / individuals. LIACC hopes this will improve research in this area so as to reach the goal of creating an agent that surpasses the best human players.

Research paper thumbnail of Artificial intelligence techniques in games with incomplete information : opponent modelling in Texas Hold'em

Research paper thumbnail of Opponent Modelling in Texas Hold'em Poker as the Key for Success

Ecai 2008, Proceedings, 2008

Abstract. Over the last few years, research in Artificial Intelligence has focussed on games with... more Abstract. Over the last few years, research in Artificial Intelligence has focussed on games with incomplete information and non-deterministic moves. The game of Poker is a perfect theme for studying this subject. The best known Poker variant is Texas Hold'em that combines simple rules with a huge amount of possible playing strategies. This paper is focussed on developing algorithms for performing simple online opponent modelling in Texas Hold'em Poker enabling to select the best strategy to play against each given ...

Research paper thumbnail of An experimental approach to online opponent modeling in Texas Hold'em Poker

The game of Poker is an excellent test bed for studying opponent modeling methodologies applied t... more The game of Poker is an excellent test bed for studying opponent modeling methodologies applied to non-deterministic games with incomplete information. The most known Poker variant, Texas Hold'em Poker, combines simple rules with a huge amount of possible playing strategies. This paper is focused on developing algorithms for performing simple online opponent modeling in Texas Hold'em. The opponent modeling approach developed enables to select the best strategy to play against each given opponent. Several autonomous agents were developed in order to simulate typical Poker player's behavior and one other agent, was developed capable of using simple opponent modeling techniques in order to select the best playing strategy against each of the other opponents. Results achieved in realistic experiments using eight distinct poker playing agents showed the usefulness of the approach. The observer agent developed is clearly capable of outperforming all its counterparts in all the experiments performed.

Research paper thumbnail of Computer Poker Research at LIACC

Cornell University - arXiv, Jan 24, 2013

Computer suited challenge for research in artificial intelligence. For that reason, and due to th... more Computer suited challenge for research in artificial intelligence. For that reason, and due to the Poker popularity in Portugal since 2008, several member of LIACC have researched in this field. Several works were published as papers and master theses and more recently a member of LIACC engaged on a research in this area as a Ph.D. thesis in order to develop a more extensive and in-depth work. This paper describes the existing research in LIACC about Computer Poker, with special emphasis on the completed master's theses and plans for future work. This paper means to present a summary of community in order to encourage the exchange of ideas with other labs / individuals. LIACC hopes this will improve research in this area so as to reach the goal of creating an agent that surpasses the best human players.

Research paper thumbnail of Artificial intelligence techniques in games with incomplete information : opponent modelling in Texas Hold'em

Research paper thumbnail of Opponent Modelling in Texas Hold'em Poker as the Key for Success

Ecai 2008, Proceedings, 2008

Abstract. Over the last few years, research in Artificial Intelligence has focussed on games with... more Abstract. Over the last few years, research in Artificial Intelligence has focussed on games with incomplete information and non-deterministic moves. The game of Poker is a perfect theme for studying this subject. The best known Poker variant is Texas Hold'em that combines simple rules with a huge amount of possible playing strategies. This paper is focussed on developing algorithms for performing simple online opponent modelling in Texas Hold'em Poker enabling to select the best strategy to play against each given ...

Research paper thumbnail of An experimental approach to online opponent modeling in Texas Hold'em Poker

The game of Poker is an excellent test bed for studying opponent modeling methodologies applied t... more The game of Poker is an excellent test bed for studying opponent modeling methodologies applied to non-deterministic games with incomplete information. The most known Poker variant, Texas Hold'em Poker, combines simple rules with a huge amount of possible playing strategies. This paper is focused on developing algorithms for performing simple online opponent modeling in Texas Hold'em. The opponent modeling approach developed enables to select the best strategy to play against each given opponent. Several autonomous agents were developed in order to simulate typical Poker player's behavior and one other agent, was developed capable of using simple opponent modeling techniques in order to select the best playing strategy against each of the other opponents. Results achieved in realistic experiments using eight distinct poker playing agents showed the usefulness of the approach. The observer agent developed is clearly capable of outperforming all its counterparts in all the experiments performed.

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