Practical Issues in Temporal Difference Learning
Machine Learning
Temporal difference learning and TD-Gammon
Communications of the ACM
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
One jump ahead: challenging human supremacy in checkers
One jump ahead: challenging human supremacy in checkers
Blondie24: playing at the edge of AI
Blondie24: playing at the edge of AI
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Programming backgammon using self-teaching neural nets
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Kasparov Vs. Deep Blue: Computer Chess Comes of Age
Kasparov Vs. Deep Blue: Computer Chess Comes of Age
Evolving Neural Networks to Play Go
Applied Intelligence
Forming neural networks through efficient and adaptive coevolution
Evolutionary Computation
Artificial Intelligence for Advanced Problem Solving Techniques
Artificial Intelligence for Advanced Problem Solving Techniques
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
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. II: recent progress
IBM Journal of Research and Development
Evolving an expert checkers playing program without using humanexpertise
IEEE Transactions on Evolutionary Computation
Imperfect Evolutionary Systems
IEEE Transactions on Evolutionary Computation
Evolving neural networks to play checkers without relying on expert knowledge
IEEE Transactions on Neural Networks
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Evolving self-learning players has attracted a lot of research attention in recent years. Fogel's Blondie24 represents one of the successes in this field and a strong motivating factor for other scientists. In this paper evolutionary neural networks, evolved via an evolution strategy, are utilised to evolve game playing strategies for the game of checkers by introducing a league structure into the learning phase of a system based on Blondie24. We believe that this helps eliminate some of the randomness in the evolution. Thirty feed forward neural network players are played against each other, using a round robin tournament structure, for 150 generations and the best player obtained is tested against a reimplementation of Blondie24. We also test the best player against an online program, as well as two other strong programs. The results obtained are promising.