A statistical foundation for machine learning, with application to Go-Moku

a new type-ii fuzzy logic based controller for non-linear dynamical systems with application to 3-psp parallel robot

abstract type-ii fuzzy logic has shown its superiority over traditional fuzzy logic when dealing with uncertainty. type-ii fuzzy logic controllers are however newer and more promising approaches that have been recently applied to various fields due to their significant contribution especially when the noise (as an important instance of uncertainty) emerges. during the design of type- i fuz...

Statistical methods with application to machine learning and artificial intelligence

A Scalable Machine Learning Approach to Go

Go is an ancient board game that poses unique opportunities and challenges for AI and machine learning. Here we develop a machine learning approach to Go, and related board games, focusing primarily on the problem of learning a good evaluation function in a scalable way. Scalability is essential at multiple levels, from the library of local tactical patterns, to the integration of patterns acro...

Go-Moku Solved by New Search Techniques

Many decades ago, Japanese professional Go-Moku players stated that Go-Moku (Five-in-a-row on horizontally placed 15×15 board) is a won game for the player to move fast. So far, this claim has never been substantiated by (a tree of) variations or by computer program. Meanwhile many variants of Go-Moku with slightly different rules have been developed. This paper shows that for two common varian...

A foundation for machine learning in design

This paper presents a formalism for considering the issues of learning in design. A foundation for machine learning in design (MLinD) is defined so as to provide answers to basic questions on learning in design, such as, “What types of knowledge can be learnt?”, “How does learning occur?”, and “When does learning occur?”. Five main elements of MLinD are presented as the input knowledge, knowled...