Title :
Boosting Blackjack Returns with Machine Learned Betting Criteria
Author_Institution :
Dept. of Comput. Sci., Marist Coll., Poughkeepsie, NY
Abstract :
This paper investigates a new method to boost Blackjack betting returns. We show betting criteria identified by genetic algorithm significantly outperform standard game theoretic criteria on ten different professional counting systems
Keywords :
game theory; genetic algorithms; learning (artificial intelligence); Blackjack betting return boosting; betting game theoretic criteria; counting system; game theory; genetic algorithm; machine learning; Boosting; Cellular phones; Computer science; Consumer electronics; Educational institutions; Game theory; Genetic algorithms; Machine learning; Mobile computing; Personal digital assistants;
Conference_Titel :
Information Technology: New Generations, 2006. ITNG 2006. Third International Conference on
Conference_Location :
Las Vegas, NV
Print_ISBN :
0-7695-2497-4
DOI :
10.1109/ITNG.2006.40
