2,551 research outputs found
Poker as a Skill Game: Rational vs Irrational Behaviors
In many countries poker is one of the most popular card games. Although each
variant of poker has its own rules, all involve the use of money to make the
challenge meaningful. Nowadays, in the collective consciousness, some variants
of poker are referred to as games of skill, others as gambling. A poker table
can be viewed as a psychology lab, where human behavior can be observed and
quantified. This work provides a preliminary analysis of the role of
rationality in poker games, using a stylized version of Texas Hold'em. In
particular, we compare the performance of two different kinds of players, i.e.,
rational vs irrational players, during a poker tournament. Results show that
these behaviors (i.e., rationality and irrationality) affect both the outcomes
of challenges and the way poker should be classified.Comment: 15 pages, 5 figure
Multiparty Dynamics and Failure Modes for Machine Learning and Artificial Intelligence
An important challenge for safety in machine learning and artificial
intelligence systems is a~set of related failures involving specification
gaming, reward hacking, fragility to distributional shifts, and Goodhart's or
Campbell's law. This paper presents additional failure modes for interactions
within multi-agent systems that are closely related. These multi-agent failure
modes are more complex, more problematic, and less well understood than the
single-agent case, and are also already occurring, largely unnoticed. After
motivating the discussion with examples from poker-playing artificial
intelligence (AI), the paper explains why these failure modes are in some
senses unavoidable. Following this, the paper categorizes failure modes,
provides definitions, and cites examples for each of the modes: accidental
steering, coordination failures, adversarial misalignment, input spoofing and
filtering, and goal co-option or direct hacking. The paper then discusses how
extant literature on multi-agent AI fails to address these failure modes, and
identifies work which may be useful for the mitigation of these failure modes.Comment: 12 Pages, This version re-submitted to Big Data and Cognitive
Computing, Special Issue "Artificial Superintelligence: Coordination &
Strategy
Simulation of a Texas Hold'Em poker player
Copyright 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is the accepted version of the article. The published version is available at
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
Computing card probabilities in Texas Hold'em
Developing Poker agents that can compete at the level of a human expert can be a challenging endeavor, since agents' strategies must be capable of dealing with hidden information, deception and risk management. A way of addressing this issue is to model opponents' behavior in order to estimate their game plan and make decisions based on such estimations. In this paper, several hand evaluation and classification techniques are compared and conclusions on their respective applicability and scope are drawn. Also, we suggest improvements on current techniques through Monte Carlo sampling. The current methods to deal with risk management were found to be pertinent concerning the agent's decision-making process; nevertheless future integration of these methods with opponent modeling techniques can greatly improve overall Poker agents' performance
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
- …