1,868 research outputs found
EvoTanks: co-evolutionary development of game-playing agents
This paper describes the EvoTanks research project, a continuing attempt to develop strong AI players for a primitive 'Combat' style video game using evolutionary computational methods with artificial neural networks. A small but challenging feat due to the necessity for agent's actions to rely heavily on opponent behaviour. Previous investigation has shown the agents are capable of developing high performance behaviours by evolving against scripted opponents; however these are local to the trained opponent. The focus of this paper shows results from the use of co-evolution on the same population. Results show agents no longer succumb to trappings of local maxima within the search space and are capable of converging on high fitness behaviours local to their population without the use of scripted opponents
An intelligent poker-agent for Texas Hold'em
Estágio realizado em Budapest University of Technology and Economics e orientado por Dániel László KovácsTese de mestrado integrado. Engenharia Informátca e Computação. Faculdade de Engenharia. Universidade do Porto. 200
Learning in Repeated Games: Human Versus Machine
While Artificial Intelligence has successfully outperformed humans in complex
combinatorial games (such as chess and checkers), humans have retained their
supremacy in social interactions that require intuition and adaptation, such as
cooperation and coordination games. Despite significant advances in learning
algorithms, most algorithms adapt at times scales which are not relevant for
interactions with humans, and therefore the advances in AI on this front have
remained of a more theoretical nature. This has also hindered the experimental
evaluation of how these algorithms perform against humans, as the length of
experiments needed to evaluate them is beyond what humans are reasonably
expected to endure (max 100 repetitions). This scenario is rapidly changing, as
recent algorithms are able to converge to their functional regimes in shorter
time-scales. Additionally, this shift opens up possibilities for experimental
investigation: where do humans stand compared with these new algorithms? We
evaluate humans experimentally against a representative element of these
fast-converging algorithms. Our results indicate that the performance of at
least one of these algorithms is comparable to, and even exceeds, the
performance of people
Machine learning applied to the context of Poker
A combinação de princípios da teoria de jogo e metodologias de machine learning aplicados ao contexto de formular estratégias ótimas para jogos está a angariar interesse por parte de uma porção crescentemente significativa da comunidade científica, tornando-se o jogo do Poker num candidato de estudo popular devido à sua natureza de informação imperfeita. Avanços nesta área possuem vastas aplicações em cenários do mundo real, e a área de investigação de inteligência artificial demonstra que o interesse relativo a este objeto de estudo está longe de desaparecer, com investigadores do Facebook e Carnegie Mellon a apresentar, em 2019, o primeiro agente de jogo autónomo de Poker provado como ganhador num cenário com múltiplos jogadores, uma conquista relativamente à anterior especificação do estado da arte, que fora desenvolvida para jogos de apenas 2 jogadores. Este estudo pretende explorar as características de jogos estocásticos de informação imperfeita, recolhendo informação acerca dos avanços nas metodologias disponibilizados por parte de investigadores de forma a desenvolver um agente autónomo de jogo que se pretende inserir na classificação de "utility-maximizing decision-maker".The combination of game theory principles and machine learning methodologies applied to encountering optimal strategies for games is garnering interest from an increasing large portion of the scientific community, with the game of Poker being a popular study subject due to its imperfect information nature. Advancements in this area have a wide array of applications in real-world scenarios, and the field of artificial intelligent studies show that the interest regarding this object of study is yet to fade, with researchers from Facebook and Carnegie Mellon presenting, in 2019, the world’s first autonomous Poker playing agent that is proven to be profitable while confronting multiple players at a time, an achievement in relation to the previous state of the art specification, which was developed for two player games only. This study intends to explore the characteristics of stochastic games of imperfect information, gathering information regarding the advancements in methodologies made available by researchers in order to ultimately develop an autonomous agent intended to adhere to the classification of a utility-maximizing decision-maker
Using a high-level language to build a poker playing agent
Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 200
Poker Learner: Reinforcement Learning Applied to Texas Hold'em Poker
Bibliografia: p. 61-66Tese de Mestrado Integrado. Engenharia Informática e Computação. Universidade do Porto. Faculdade de Engenharia.. 201
Approximate exploitability: Learning a best response in large games
A standard metric used to measure the approximate optimality of policies in
imperfect information games is exploitability, i.e. the performance of a policy
against its worst-case opponent. However, exploitability is intractable to
compute in large games as it requires a full traversal of the game tree to
calculate a best response to the given policy. We introduce a new metric,
approximate exploitability, that calculates an analogous metric using an
approximate best response; the approximation is done by using search and
reinforcement learning. This is a generalization of local best response, a
domain specific evaluation metric used in poker. We provide empirical results
for a specific instance of the method, demonstrating that our method converges
to exploitability in the tabular and function approximation settings for small
games. In large games, our method learns to exploit both strong and weak
agents, learning to exploit an AlphaZero agent
Building a poker playing agent based on game logs using supervised learning
Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201
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