1,802 research outputs found
Building a computer poker agent with emphasis on opponent modeling
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (pages 53-54).In this thesis, we present a computer agent for the game of no-limit Texas Hold'em Poker for two players. Poker is a partially observable, stochastic, multi-agent, sequential game. This combination of characteristics makes it a very challenging game to master for both human and computer players. We explore this problem from an opponent modeling perspective, using data mining to build a database of player styles that allows our agent to quickly model the strategy of any new opponent. The opponent model is then used to develop a robust counter strategy. A simpler version of this agent modified for a three player game was able to win the 2011 MIT Poker Bot Competition.by Jian Huang.M. Eng
Solving Large Extensive-Form Games with Strategy Constraints
Extensive-form games are a common model for multiagent interactions with
imperfect information. In two-player zero-sum games, the typical solution
concept is a Nash equilibrium over the unconstrained strategy set for each
player. In many situations, however, we would like to constrain the set of
possible strategies. For example, constraints are a natural way to model
limited resources, risk mitigation, safety, consistency with past observations
of behavior, or other secondary objectives for an agent. In small games,
optimal strategies under linear constraints can be found by solving a linear
program; however, state-of-the-art algorithms for solving large games cannot
handle general constraints. In this work we introduce a generalized form of
Counterfactual Regret Minimization that provably finds optimal strategies under
any feasible set of convex constraints. We demonstrate the effectiveness of our
algorithm for finding strategies that mitigate risk in security games, and for
opponent modeling in poker games when given only partial observations of
private information.Comment: Appeared in AAAI 201
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
Traditional Wisdom and Monte Carlo Tree Search Face-to-Face in the Card Game Scopone
We present the design of a competitive artificial intelligence for Scopone, a
popular Italian card game. We compare rule-based players using the most
established strategies (one for beginners and two for advanced players) against
players using Monte Carlo Tree Search (MCTS) and Information Set Monte Carlo
Tree Search (ISMCTS) with different reward functions and simulation strategies.
MCTS requires complete information about the game state and thus implements a
cheating player while ISMCTS can deal with incomplete information and thus
implements a fair player. Our results show that, as expected, the cheating MCTS
outperforms all the other strategies; ISMCTS is stronger than all the
rule-based players implementing well-known and most advanced strategies and it
also turns out to be a challenging opponent for human players.Comment: Preprint. Accepted for publication in the IEEE Transaction on Game
Simulation of a Texas Hold'Em poker player
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