Coevolutionary search among adversaries
Coevolutionary search among adversaries
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Handbook of Computational Economics
Handbook of Computational Economics
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Global Optimisation by Evolutionary Algorithms
PAS '97 Proceedings of the 2nd AIZU International Symposium on Parallel Algorithms / Architecture Synthesis
N-tuple Network, CART, and Bagging
Neural Computation
Application of reinforcement learning to the game of Othello
Computers and Operations Research
Pattern recognition and reading by machine
IRE-AIEE-ACM '59 (Eastern) Papers presented at the December 1-3, 1959, eastern joint IRE-AIEE-ACM computer conference
Focusing versus intransitivity: geometrical aspects of co-evolution
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
A game-theoretic memory mechanism for coevolution
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Evolving artificial neural network ensembles
IEEE Computational Intelligence Magazine
Observing the evolution of neural networks learning to play the game of Othello
IEEE Transactions on Evolutionary Computation
Measuring Generalization Performance in Coevolutionary Learning
IEEE Transactions on Evolutionary Computation
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Cycling has been an obstacle to coevolution of machine-learning agents. Monotonic algorithms seek continual improvement with respect to a solution concept; seeking an agent or set of agents that approaches the true solution without cycling. Algorithms that guarantee monotonicity generally require unlimited storage. One such algorithm is the Nash Memory, which uses the Nash Equilibrium as the solution concept. The requirement for unbounded storage is an obstacle to the use of this algorithm in large applications. This paper demonstrates the performance of the Nash Memory algorithm with fixed storage in coevolving a population of moderately large agents (with knowledge represented as n-tuple networks) learning a function with a large state space (an evaluation function for the game of Othello). The success of the algorithm results from the diversity of the agents produced, and the corresponding need for improved global performance in order for agents to survive and reproduce. The algorithm can be expected to converge to a region of highest performance within the capability of the search operators.