Representations and solutions for game-theoretic problems
Artificial Intelligence - Special issue on economic principles of multi-agent systems
One jump ahead: challenging human supremacy in checkers
One jump ahead: challenging human supremacy in checkers
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Using probabilistic knowledge and simulation to play poker
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Studies in machine cognition using the game of poker
Communications of the ACM
Blondie24: playing at the edge of AI
Blondie24: playing at the edge of AI
Poker as Testbed for AI Research
AI '98 Proceedings of the 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Some studies in machine learning using the game of checkers
IBM Journal of Research and Development
Some studies in machine learning using the game of checkers
IBM Journal of Research and Development
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Construction of poker playing system considering strategies
Proceedings of the 2006 international conference on Game research and development
Evolving defence strategies by genetic programming
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
| Hi-index | 0.00 |
Other work has shown that adaptive learning can be highly successful in developing programs which are able to play games at a level similar to human players and, in some cases, exceed the ability of a vast majority of human players. This study uses poker to investigate how adaptation can be used in games of imperfect information. An internal learning value is manipulated which allows a poker playing agent to develop its playing strategy over time. The results suggest that the agent is able to learn how to play poker, initially losing, before winning as the players strategy becomes more developed. The evolved player performs well against opponents with different playing styles. Some limitations of previous work are overcome, such as deal rotation to remove the bias introduced by one player always being the last to act. This work provides encouragement that this is an area worth exploring more fully in our future work.