TsetlinGo : Solving the game of Go with Tsetlin Machine

Master's thesis in Information- and communication technology (IKT590)The Tsetlin Machine have already shown great promise on pattern recognition and text categorization. The board game GO is a highly complex game, and the Tsetlin Machine have not yet been tested extensively on strategic games like this. This thesis introduces TsetlinGO and aims to Solve the game of Go with Tsetlin Machine. For predicting the next moves a combination of Tsetlin Machine and Tree Search was used. In the thesis a 9x9 board size was used for the game of Go, to prevent the problem from becoming to complex. This thesis goes through hyper-parameter testing for classification of the Go board game. A solution with Tree Search and Tsetlin Machine combined is used to perform self-play and matches between Tsetlin Machines with different hyper-parameters. Based on the empirical results, our conclusion is that the Tsetlin Machine is more than capable for classification of the game of Go at various stages of play. Results from the experiments could be seen to achieve around 90%, while further climbing up to around 95% upon re-training. From examining the clauses, strong patterns was found that gave insight into how the machine works. The Tsetlin Machine was able to play complete games of Go, making connections on the board through use of patterns from the clauses. It was found that the size of the clauses had great impact as clauses with large patterns had trouble getting triggered in early play. The high accuracy from classification was found to not correlate with how strong the Tsetlin Machine would perform during self-play. This may indicate that producing training data directly from self-play may be required to fine tune the assessment of board positions faced during actual play. We can conclude that this thesis provide a benchmark for further research within the field of Tsetlin Machine and the game of Go

TsetlinGo : Solving the game of Go with Tsetlin Machine

The Tsetlin Machine have already shown great promise on pattern recognition and text categorization. The board game GO is a highly complex game, and the Tsetlin Machine have not yet been tested extensively on strategic games like this. This thesis introduces TsetlinGO and aims to Solve the game of Go with Tsetlin Machine. For predicting the next moves a combination of Tsetlin Machine and Tree Search was used. In the thesis a 9x9 board size was used for the game of Go, to prevent the problem from becoming to complex. This thesis goes through hyper-parameter testing for classification of the Go board game. A solution with Tree Search and Tsetlin Machine combined is used to perform self-play and matches between Tsetlin Machines with different hyper-parameters. Based on the empirical results, our conclusion is that the Tsetlin Machine is more than capable for classification of the game of Go at various stages of play. Results from the experiments could be seen to achieve around 90%, while further climbing up to around 95% upon re-training. From examining the clauses, strong patterns was found that gave insight into how the machine works. The Tsetlin Machine was able to play complete games of Go, making connections on the board through use of patterns from the clauses. It was found that the size of the clauses had great impact as clauses with large patterns had trouble getting triggered in early play. The high accuracy from classification was found to not correlate with how strong the Tsetlin Machine would perform during self-play. This may indicate that producing training data directly from self-play may be required to fine tune the assessment of board positions faced during actual play. We can conclude that this thesis provide a benchmark for further research within the field of Tsetlin Machine and the game of Go

Simulation of infrared avalanche photodiodes from first principles

The present article deals with device physics and modeling of an Hg0.28Cd0.72Te wide-area electron-initiated avalanche photodiode, with main input data extracted from first principles electronic structure codes. Due to the large dimensions of 30 µm x 30 µm x 11 µm a method which combines Monte Carlo transport simulation in the active multiplication layer with ‘weak conduction’ modeling in the charge carrier exit paths is introduced. Consequences resulting from adding perturbative, non-self-consistent small-signal analyses upon a self-consistent, large-signal background bias simulation are briefly examined. Likewise, the issue of ambipolar versus independent electron-hole transport in the absorption layer is discussed. We investigate the effects of alloy scattering on avalanche gain and compare alloy scattering rates used in some recent studies. Alloy scattering is for this particular device and model shown to increase the gain by more than an order of magnitude at typical bias voltages