Blondie24: playing at the edge of AI
Blondie24: playing at the edge of AI
Evolving neural networks through augmenting topologies
Evolutionary Computation
Continual Coevolution Through Complexification
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
A Taxonomy for artificial embryogeny
Artificial Life
Evolving modular genetic regulatory networks
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Compositional pattern producing networks: A novel abstraction of development
Genetic Programming and Evolvable Machines
Combining online and offline knowledge in UCT
Proceedings of the 24th international conference on Machine learning
Sample-based learning and search with permanent and transient memories
Proceedings of the 25th international conference on Machine learning
A case study on the critical role of geometric regularity in machine learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Reinforcement learning of local shape in the game of go
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Scalable Neural Networks for Board Games
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Multi-dimensional recurrent neural networks
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Evolving neural networks for geometric game-tree pruning
Proceedings of the 13th annual conference on Genetic and evolutionary computation
An evolutionary multi-objective optimization approach to computer go controller synthesis
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Single-unit pattern generators for quadruped locomotion
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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The game of Go has attracted much attention from the artificial intelligence community. A key feature of Go is that humans begin to learn on a small board, and then incrementally learn advanced strategies on larger boards. While some machine learning methods can also scale the board, they generally only focus on a subset of the board at one time. Neuroevolution algorithms particularly struggle with scalable Go because they are often directly encoded (i.e. a single gene maps to a single connection in the network). Thus this paper applies an indirect encoding to the problem of scalable Go that can evolve a solution to 5×5 Go and then extrapolate that solution to 7×7 Go and continue evolution. The scalable method is demonstrated to learn faster and ultimately discover better strategies than the same method trained on 7×7 Go directly from the start.