intelligence
Introduction to creative evolutionary systems
Creative evolutionary systems
Beyond Samuel: evolving a nearly expert checkers player
Advances in evolutionary computing
Evolving intelligent game-playing agents
SAICSIT '03 Proceedings of the 2003 annual research conference of the South African institute of computer scientists and information technologists on Enablement through technology
Tuning evaluation functions by maximizing concordance
Theoretical Computer Science - Advances in computer games
Universal parameter optimisation in games based on SPSA
Machine Learning
Introductory tutorial on coevolution
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Levelling the playing field: games handicapping
MIV'06 Proceedings of the 6th WSEAS International Conference on Multimedia, Internet & Video Technologies
Simulation Analysis Using Multi-Agent Systems for Social Norms
Computational Economics
An Adaptive Global-Local Memetic Algorithm to Discover Resources in P2P Networks
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Introducing a round robin tournament into Blondie24
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
An evolutionary neural network approach to intrinsic plagiarism detection
AICS'09 Proceedings of the 20th Irish conference on Artificial intelligence and cognitive science
Abandoning objectives: Evolution through the search for novelty alone
Evolutionary Computation
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Fuzzeval: a fuzzy controller-based approach in adaptive learning for backgammon game
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
RSPSA: enhanced parameter optimization in games
ACG'05 Proceedings of the 11th international conference on Advances in Computer Games
Evolving team behaviors with specialization
Genetic Programming and Evolvable Machines
A multimodal problem for competitive coevolution
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Simulation Analysis for Network Formulation
Computational Economics
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An experiment was conducted where neural networks compete for survival in an evolving population based on their ability to play checkers. More specifically, multilayer feedforward neural networks were used to evaluate alternative board positions and games were played using a minimax search strategy. At each generation, the extant neural networks were paired in competitions and selection was used to eliminate those that performed poorly relative to other networks. Offspring neural networks were created from the survivors using random variation of all weights and bias terms. After a series of 250 generations, the best-evolved neural network was played against human opponents in a series of 90 games on an Internet website. The neural network was able to defeat two expert-level players and played to a draw against a master. The final rating of the neural network placed it in the “Class A” category using a standard rating system. Of particular importance in the design of the experiment was the fact that no features beyond the piece differential were given to the neural networks as a priori knowledge. The process of evolution was able to extract all of the additional information required to play at this level of competency. It accomplished this based almost solely on the feedback offered in the final aggregated outcome of each game played (i.e., win, lose, or draw). This procedure stands in marked contrast to the typical artifice of explicitly injecting expert knowledge into a game-playing program