Opponent modeling in poker (1998) [36 citations — 8 self]
|
Popular Tags
No tags have been applied to this document.
BibTeX | Add To MetaCart
@INPROCEEDINGS{Davidson98opponentmodeling,author = {Aaron Davidson and Darse Billings and Duane Szafron},
title = {Opponent modeling in poker},
booktitle = {In AAAI National Conference},
year = {1998},
pages = {493--499}
}
Years of Citing Articles
Abstract:
Abstract The game of poker has many properties that make it an interesting topic for arti cial intelligence (AI). It is a game of imperfect information, which relates to one of the most fundamental problems in computer science: how to handle knowledge that may be erroneous or incomplete. Poker is also one of the few games to be studied where deriving an accurate understanding of each opponent's style is an essential element to success. In developing a strong poker program, the opponent modeling method has always been a central component of the system. As other aspects of the program were improved, the techniques for modeling once again became a limiting factor to the overall level of play. As a result, the topic has been revisited. This paper reports on recent progress achieved by improved statistical methods, which were suggested by experiments using articial neural networks.
Citations
| 80 | Representations and Solutions for GameTheoretic Problems – Koller, Pfeffer |
| 37 | Incorporating Opponent Models into Adversary Search, AAAI – Carmel, Markovitch - 1995 |
| 29 | Using Probabilistic Knowledge and Simulation to Play Poker – Billings, Peña, et al. - 1999 |
| 20 | Using Knowledge About the Opponent in Game-Tree Search – Jansen - 1992 |
| 10 | Hold’em Poker for Advanced Players, Two Plus Two – Sklansky, Malmuth - 1994 |
| 6 | Thoughts on the application of opponent-model search – Iida, Uiterwijk, et al. - 1995 |
| 1 | Using arti neural networks to model opponents in Texas Hold'em – Davidson - 1999 |
| 1 | Poker as a Testbed for AI Research, AI'98 – Billings, Papp, et al. - 1997 |
