9 research outputs found
Evaluating Go Game Records for Prediction of Player Attributes
We propose a way of extracting and aggregating per-move evaluations from sets
of Go game records. The evaluations capture different aspects of the games such
as played patterns or statistic of sente/gote sequences. Using machine learning
algorithms, the evaluations can be utilized to predict different relevant
target variables. We apply this methodology to predict the strength and playing
style of the player (e.g. territoriality or aggressivity) with good accuracy.
We propose a number of possible applications including aiding in Go study,
seeding real-work ranks of internet players or tuning of Go-playing programs
MCTS with Information Sharing
We introduce our competitive implementation of a Monte Carlo Tree Search (MCTS) algorithm for the board game of Go: Pachi. The software is based both on previously published methods and our original improvements. We then focus on improving the tree search performance by collecting information regarding tactical situations and game status from the Monte Carlo simulations and sharing it with and within the game tree. We propose specific methods of such sharing --- dynamic komi, criticality-based biasing, and liberty maps --- and demonstrate their positive effect. based on collected play-testing measurements. We also outline some promising future research directions related to our work
Current Concepts in Version Control Systems
We will describe and analyse the concepts, architectural approaches and methods currently in use in the eld of version control systems, present some original work in the area and propose and outline interesting future research directions
MCTS with Information Sharing
We introduce our competitive implementation of a Monte Carlo Tree Search (MCTS) algorithm for the board game of Go: Pachi. The software is based both on previously published methods and our original improvements. We then focus on improving the tree search performance by collecting information regarding tactical situations and game status from the Monte Carlo simulations and sharing it with and within the game tree. We propose specific methods of such sharing --- dynamic komi, criticality-based biasing, and liberty maps --- and demonstrate their positive effect. based on collected play-testing measurements. We also outline some promising future research directions related to our work