Imperfect Information Games

2018 ◽  
pp. 209-269
Author(s):  
Susheng Wang
Keyword(s):  
Imperfect Information ◽  
Imperfect Information Games ◽  
Information Games

scholarly journals Limited lookahead in imperfect-information games

2020 ◽  
Vol 283 ◽  
pp. 103218 ◽  
Author(s):  
Christian Kroer ◽  
Tuomas Sandholm
Keyword(s):  
Imperfect Information ◽  
Imperfect Information Games ◽  
Information Games

A Decision Making Method Based on Society of Mind Theory in Multi-Player Imperfect Information Games

2020 ◽  
pp. 317-329
Author(s):  
Mitsuo Wakatsuki ◽  
Mari Fujimura ◽  
Tetsuro Nishino
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Imperfect Information ◽  
Card Game ◽  
Mind Theory ◽  
Imperfect Information Games ◽  
Game Players ◽  
The University ◽  
Society Of Mind ◽  
Information Games

The authors are concerned with a card game called Daihinmin (Extreme Needy), which is a multi-player imperfect information game. Using Marvin Minsky's “Society of Mind” theory, they attempt to model the workings of the minds of game players. The UEC Computer Daihinmin Competitions (UECda) have been held at the University of Electro-Communications since 2006, to bring together competitive client programs that correspond to players of Daihinmin, and contest their strengths. In this paper, the authors extract the behavior of client programs from actual competition records of the computer Daihinmin, and propose a method of building a system that determines the parameters of Daihinmin agencies by machine learning.

Game-Tree Search with Adaptation in Stochastic Imperfect-Information Games

2006 ◽  
pp. 21-34 ◽  
Author(s):  
Darse Billings ◽  
Aaron Davidson ◽  
Terence Schauenberg ◽  
Neil Burch ◽  
Michael Bowling ◽  
...  
Keyword(s):  
Imperfect Information ◽  
Tree Search ◽  
Game Tree ◽  
Imperfect Information Games ◽  
Game Tree Search ◽  
Information Games

scholarly journals Enhancing Artificial Intelligence on a Real Mobile Game

2009 ◽  
Vol 2009 ◽  
pp. 1-9 ◽  
Author(s):  
Fabio Aiolli ◽  
Claudio E. Palazzi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Mobile Phones ◽  
Imperfect Information ◽  
Learning Algorithm ◽  
Predictive Performance ◽  
Human Opponent ◽  
Imperfect Information Games ◽  
Game State ◽  
Information Games

Mobile games represent a killer application that is attracting millions of subscribers worldwide. One of the aspects crucial to the commercial success of a game is ensuring an appropriately challenging artificial intelligence (AI) algorithm against which to play. However, creating this component is particularly complex as classic search AI algorithms cannot be employed by limited devices such as mobile phones or, even on more powerful computers, when considering imperfect information games (i.e., games in which participants do not a complete knowledge of the game state at any moment). In this paper, we propose to solve this issue by resorting to a machine learning algorithm which uses profiling functionalities in order to infer the missing information, thus making the AI able to efficiently adapt its strategies to the human opponent. We studied a simple and computationally light machine learning method that can be employed with success, enabling AI improvements for imperfect information games even on mobile phones. We created a mobile phone-based version of a game calledGhostsand present results which clearly show the ability of our algorithm to quickly improve its own predictive performance as far as the number of games against the same human opponent increases.

scholarly journals Learning to Play Imperfect-Information Games by Imitating an Oracle Planner

2021 ◽  
pp. 1-1
Author(s):  
Rinu Boney ◽  
Alexander Ilin ◽  
Juho Kannala ◽  
Jarno Seppanen
Keyword(s):  
Imperfect Information ◽  
Imperfect Information Games ◽  
Information Games
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