CADET: a case-based synthesis tool for engineering design
International Journal of Expert Systems - Special issue on case-based reasoning
Case-based reasoning
Applying case-based reasoning: techniques for enterprise systems
Applying case-based reasoning: techniques for enterprise systems
Inside Case-Based Reasoning
Artificial Intelligence Review - Special issue on lazy learning
Excessive Gap Technique in Nonsmooth Convex Minimization
SIAM Journal on Optimization
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Case-Based Planning and Execution for Real-Time Strategy Games
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Situation Assessment for Plan Retrieval in Real-Time Strategy Games
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Abstraction pathologies in extensive games
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
On-line case-based plan adaptation for real-time strategy games
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Transfer learning in real-time strategy games using hybrid CBR/RL
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Approximating game-theoretic optimal strategies for full-scale poker
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Probabilistic state translation in extensive games with large action sets
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Monte-Carlo Tree Search in Poker Using Expected Reward Distributions
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Gradient-based algorithms for finding Nash equilibria in extensive form games
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Using counterfactual regret minimization to create competitive multiplayer poker agents
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Learning to win: case-based plan selection in a real-time strategy game
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Similarity-Based retrieval and solution re-use policies in the game of texas hold'em
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
Game-Tree search with adaptation in stochastic imperfect-information games
CG'04 Proceedings of the 4th international conference on Computers and Games
On combining decisions from multiple expert imitators for performance
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
| Hi-index | 0.00 |
The state-of-the-art within Artificial Intelligence has directly benefited from research conducted within the computer poker domain. One such success has been the advancement of bottom up equilibrium finding algorithms via computational game theory. On the other hand, alternative top down approaches, that attempt to generalise decisions observed within a collection of data, have not received as much attention. In this work we employ a top down approach in order to construct case-based strategies within three computer poker domains. Our analysis begins within the simplest variation of Texas Hold'em poker, i.e. two-player, limit Hold'em. We trace the evolution of our case-based architecture and evaluate the effect that modifications have on strategy performance. The end result of our experimentation is a coherent framework for producing strong case-based strategies based on the observation and generalisation of expert decisions. The lessons learned within this domain offer valuable insights, that we use to apply the framework to the more complicated domains of two-player, no-limit Hold'em and multi-player, limit Hold'em. For each domain we present results obtained from the Annual Computer Poker Competition, where the best poker agents in the world are challenged against each other. We also present results against human opposition.