The History Heuristic and Alpha-Beta Search Enhancements in Practice
IEEE Transactions on Pattern Analysis and Machine Intelligence
Temporal difference learning and TD-Gammon
Communications of the ACM
Blondie24: playing at the edge of AI
Blondie24: playing at the edge of AI
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Evolving neural networks through augmenting topologies
Evolutionary Computation
Heuristic evaluation functions for general game playing
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Fluxplayer: a successful general game player
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Simulation-based approach to general game playing
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Bandit based monte-carlo planning
ECML'06 Proceedings of the 17th European conference on Machine Learning
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
Game playing has always provided an exciting avenue of research in Artificial Intelligence. Various methodologies and techniques have been developed to build intelligent game players. Coevolution has proven to be successful in learning how to play games with no prior game knowledge. In this paper we develop a coevolutionary system for the General Game Playing framework, where absolutely nothing is known about the game beforehand, and enhance it using Cultural Algorithms. In order to test the effectiveness of complementing coevolution with cultural algorithms, we play matches in several games between our player, a random player and one trained using standard coevolution.