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

Meta-learning methods for analyzing Go playing trends

This thesis extends the methodology for extracting evaluations of players from samples of Go game records originally presented in (Baudiš - Moudřík, 2012). Firstly, this work adds more features and lays out a methodology for their comparison. Secondly, we develop a robust machine-learning framework, which is able to capture dependencies between the evaluations and general target variable using ensemble meta-learning with a genetic algorithm. We apply this framework to two domains, estimation of strength and styles. The results show that the inference of the target variables in both cases is viable and reasonably precise. Finally, we present a web application, which realizes the methodology, while presenting a prototype teaching aid for the Go players and gathering more data. Powered by TCPDF (www.tcpdf.org

Meta-učící metody pro analýzu trendů her Go

Práce rozšiřuje metodiku pro ohodnocování hráčů hry go na základě záznamů jejich her, kterou jsme dříve publikovali v (Baudiš - Moudřík, 2012). V této diplomové práci jsme nejprve přidali některé featury a navrhli metodiku pro jejich porovnávání. Následně jsme představili robustní framework, který je pomocí metod strojového učení schopen zachytit závislosti mezi ohodnoceními hráčů a obecnou závislou proměnou. Tento framework spočívá v evoluci ansámblových metod strojového učení. Aplikovali jsme jej na dva problémy - predikci síly hráčů a stylů jejich hry. Výsledky ukazují, že v obou případech je možné tuto predikci provést s rozumnou přesností. Jedním z výsledků práce je i webová aplikace, která demonstruje metodiku navrženou v této práci, slouží jako pomůcka pro studium hry go a umožňuje další sběr dat. Powered by TCPDF (www.tcpdf.org)This thesis extends the methodology for extracting evaluations of players from samples of Go game records originally presented in (Baudiš - Moudřík, 2012). Firstly, this work adds more features and lays out a methodology for their comparison. Secondly, we develop a robust machine-learning framework, which is able to capture dependencies between the evaluations and general target variable using ensemble meta-learning with a genetic algorithm. We apply this framework to two domains, estimation of strength and styles. The results show that the inference of the target variables in both cases is viable and reasonably precise. Finally, we present a web application, which realizes the methodology, while presenting a prototype teaching aid for the Go players and gathering more data. Powered by TCPDF (www.tcpdf.org)Department of Theoretical Computer Science and Mathematical LogicKatedra teoretické informatiky a matematické logikyFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult

Model V1 s realistickou distribucí funkčních typů neuronů v rámci kortikálních vrstev

There have been identified two functionally different neuron classes in the primary visual cortex (V1), so-called simple and complex cells. These cells differ in reactions to various stimuli and their development has been successfully simulated in one computational model of V1. This model, however, simulates both classes in separate layers corresponding to layers 4C and 2/3 in V1. On the contrary, experiments have shown that both categories are - in different proportions - present in both layers. In this thesis, a computational model with a realistic distribution of complex and simple cells is presented. To increase its authenticity, I incorporate long-range excitatory and short-range inhibitory lateral cortical connections as found in animals, overcoming one drawback of previous models that used long-range inhibition. To assess my model, two measures of orientation selectivity - circular variance and orientation bandwidth - were computed for each simulated neuron. Using this measures, I compared my model with data from macaque monkey. In line with biological findings, my model develops a wide diversity of orientation selectivity. Moreover, it develops maps of orientation preference and realistic receptive fields