Search in games with incomplete information: a case study using Bridge card play
Ian Frank,David Basin +1 more
TLDR
It is shown that equilibrium point strategies for optimal play exist for this model, and an algorithm capable of computing such strategies is defined, and this model allows for clearly state the limitations of such architectures in producing expert analysis.About:
This article is published in Artificial Intelligence.The article was published on 1998-04-01 and is currently open access. It has received 112 citations till now. The article focuses on the topics: Bayesian game & Complete information.read more
Citations
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Dissertation
Deep Reinforcement Learning from Self-Play in Imperfect-Information Games
Johannes Heinrich,David Silver +1 more
TL;DR: This paper introduces the first scalable end-to-end approach to learning approximate Nash equilibria without prior domain knowledge, and combines fictitious self-play with deep reinforcement learning.
Journal ArticleDOI
GIB: imperfect information in a computationally challenging game
TL;DR: GIB, the program being described, involves five separate technical advances: partition search, the practical application of Monte Carlo techniques to realistic problems, a focus on achievable sets to solve problems inherent in the Monte Carlo approach, an extension of alpha-beta pruning from total orders to arbitrary distributive lattices, and the use of squeaky wheel optimization to find approximately optimal solutions to cardplay problems.
Journal ArticleDOI
Information Set Monte Carlo Tree Search
TL;DR: Three new information set MCTS (ISMCTS) algorithms are presented which handle different sources of hidden information and uncertainty in games, instead of searching minimax trees of game states, the ISMCTS algorithms search trees of information sets, more directly analyzing the true structure of the game.
Proceedings Article
GIB: Steps Toward an Expert-Level Bridge-Playing Program
TL;DR: GIB, the first bridge-playing program to approach the level of a human expert, is described and the results of experiments comparing GIB to both human opponents and other programs are presented.
Proceedings Article
Monte Carlo Planning in RTS Games.
TL;DR: A framework — MCPlan — for Monte Carlo planning is presented, its performance parameters are identified, and the results of an implementation in a capture– the–flag game are analyzed.
References
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Book
Theory of Games and Economic Behavior
TL;DR: Theory of games and economic behavior as mentioned in this paper is the classic work upon which modern-day game theory is based, and it has been widely used to analyze a host of real-world phenomena from arms races to optimal policy choices of presidential candidates, from vaccination policy to major league baseball salary negotiations.
Book ChapterDOI
Non-cooperative games
TL;DR: In this article, it was shown that the set of equilibrium points of a two-person zero-sum game can be defined as a set of all pairs of opposing "good" strategies.
Journal ArticleDOI
Games and decisions; introduction and critical survey.
R. Duncan Luce,Howard Raiffa +1 more
Book
Uncertainty in Artificial Intelligence 2
TL;DR: Qualitative Probabilistic Reasoning and Cognitive models, Dempster-Shafer Theory in Knowledge Representation, and Possibility Theory: Semantics and Applications.