Solving Go on Small Boards

Solving Ponnuki-Go on Small Boards

Indirect Encoding of Neural Networks for Scalable Go In: Proceedings of the 11th International Conference on 敳敲癥摀搠= *敳敲癥摀戠 敳敲癥摀戠-0.1in Parallel Problem Solving From Nature (PPSN 2010). New York, NY: Springer 敳敲癥摀搠= *敳敲癥摀戠 敳敲癥摀戠-0.1in

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 ...

Solving Go for Rectangular Boards

In 2003, the solution for the 5×5 Go board was published in this journal. The current article presents the game-theoretic values for rectangular boards up to a surface of 30 intersections under Chinese rules. The result was achieved by improving the αβ-based solver MIGOS. Moreover, the article identifies errors in published human solutions by comparing them with our computer solutions.

Exploring Positional Linear Go

Linear Go is the game of Go played on the 1×n board. Positional Linear Go is Linear Go with a rule set that uses positional superko. We explore gametheoretic properties of Positional Linear Go, and incorporate them into a solver based on MTD( f ) search, solving states on boards up to 1×9.

Evolving a Roving Eye for Go

Go remains a challenge for artificial intelligence. Currently, most machine learning methods tackle Go by playing on a specific fixed board size, usually smaller than the standard 19×19 board of the complete game. Because such techniques are designed to process only one board size, the knowledge gained through experience cannot be applied on larger boards. In this paper, a roving eye neural net...