The book is focused on the developments and prospective challenging problems in the area of mind game playing (i.e. playing games that require mental skills) using Computational Intelligence (CI) methods, mainly neural networks, genetic/evolutionary programming and reinforcement learning. The majority of discussed game playing ideas were...

In this book, we aim to collect the most recent advances in artificial intelligence techniques (i.e. neural networks, fuzzy systems, multi-agent systems, genetic algorithms, image analysis, clustering, etc), which are applied to the protection of privacy and security. The symbiosis between these fields leads to a pool of invigorating ideas,...

Illustrating essential aspects of adaptive image processing from a computational intelligence viewpoint, the second edition of Adaptive Image Processing: A Computational Intelligence Perspective provides an authoritative and detailed account of computational intelligence (CI) methods and algorithms for adaptive image...

The 10th International Conference on Unconventional Computation, UC 2011, was organized under the auspices of EATCS and Academia Europaea, by the Department of Mathematics of the University of Turku (Turku, Finland), and the Center for Discrete Mathematics and Theoretical Computer Science (Auckland, New Zealand). The event was held in...

Gamification may be a new term, but the idea of using game-thinking and game mechanics to solve problems and engage audiences isn’t exactly new. The military has been using games and simulations for hundreds (if not thousands) of years, and the U.S. military has been a pioneer in the use of video games across branches. Three...

The purpose of this book is to provide up-to-date progress both in Multiple Criteria Programming (MCP) and Support Vector Machines (SVMs) that have become powerful tools in the field of data mining. Most of the content in this book are directly from the research and application activities that our research group has conducted over the...

Statistical learning — that is, learning from data — and, in particular, probabilistic model learning have become increasingly important in recent years. Advances in information technology have facilitated an explosion of available data. This explosion has been accompanied by theoretical advances, permitting new and...

Computational intelligence is a fast-moving, multidisciplinary field - the nexus of diverse technical interest areas that include neural networks, fuzzy logic, and evolutionary computation. Keeping up with computational intelligence means understanding...

Recognizing patterns is at the core of man's attempts to understand the universe that we inhabit. Through the development of sensors and instruments, we have extended our ability to gather data far beyond the range and scale of man's natural senses. As a result we are now able to gather data on phenomena occurring in the far reaches...

Soft computing, as a collection of techniques exploiting approximation and tolerance for imprecision and uncertainty in traditionally intractable problems, has become very effective and popular especially because of the synergy derived from its components. The integration of constituent technologies provides complementary methods that allow...

In the early days of pattern recognition and statistical data analysis life was rather simple: datasets were relatively small, collected from well-designed experiments, analyzed using a few methods that had good theoretical background. Explosive growth of the use of computers led to the creation of huge amounts of data of all kinds,...

Wiley-Interscience Series in Discrete Mathematics and Optimization Advisory Editors Ronald L. Graham Jan Karel Lenstra Robert E. Tarjan Discrete Mathematics and Optimization involves the study of finite structures. It is one of the fastest growing areas in mathematics today. The level and depth of recent advances in the area and the wide...