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Intelligent system for card game analysis and prediction

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents a system which to make proper poker decision in situations provided by end user via image of the online poker table. The system provides feedback and reasoning behind decision made. The main goal is to minimize influences of errors and unwanted factors on each step so final decision could be accurate and useful in as many cases as possible. The idea is to use softcomputing technologies as neural networks for image recognition and expert system for decision making process. The system - able to parse poker table image - could be used by poker player for self-study on example on his/her own past in-game situations. Image is screenshot of the interface that is provided by online poker room to a player, so all information available to a player will also be available for further processing. The proposed solution can be an essential tool for the monitoring and verification of card game rules systems and to point the incorrect or illegal situations based on video data.
Rocznik
Strony
47--54
Opis fizyczny
Bibliogr. 6 poz., rys., tab., wykr.
Twórcy
  • Wroclaw University of Science and Technology, Faculty of Electronics, Poland
Bibliografia
  • [1] Bishop, Ch. M. (1995). Neural Networks for Pattern Recognition. Birmingham, UK: Clarendon Press Oxford.
  • [2] Butler, Ch. & Caudill, M. (1994). Understanding Neural Networks. MA, USA: MIT Press Cambridge.
  • [3] Damiani, E. (2004). Soft Computing in Software Engineering. Berlin, Germany: Springer.
  • [4] Joey P., Skill or Luck. http://realmoney.durrrrchallenge.com/skill-luck- paradox/.
  • [5] O’Meara, A. F., Online No-Limit Texas Hold’em For Beginners. http://www.gamblingsystem.biz/books/Online%20 No-Limit%20Texas%20Hold'em%20Poker%20 For%20Beginners%20(August%20O'%20Meara). pdf.
  • [6] Waterman, D.A. (1985). A Guide to Expert Systems. Boston, USA: Addison Wesley Publishing Company.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c50026e4-370e-4eb1-96f0-276834d990f8
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