How have Japanese companies become world leaders in the automotive and electronics industries, among others? What is the secret of their success? Two leading Japanese business experts, Ikujiro Nonaka and Hirotaka Takeuchi, are the first to tie the success of Japanese companies to their ability to create new knowledge and use it to produce successful products and technologies. …

As artificial intelligence (AI) is applied to more complex problems and a wider set of applications, the ability to take advantage of the computational power of distributed and parallel hardware architectures and to match these architec­ tures with the inherent distributed aspects of applications (spatial, functional, or temporal) has become an important research issue. Out of these research concerns, an AI subdiscipline called distributed problem solving has emerged. Distributed problem-solving systems are broadly defined as loosely-coupled, distributed networks of semi-autonomous problem-solving agents that perform sophisticated problem solving and cooperatively interact to solve problems. N odes operate asynchronously and in parallel with limited internode commu­ nication. Limited internode communication stems from either inherent band­ width limitations of the communication medium or from the high computa­ tional cost of packaging and assimilating information to be sent and received among agents. Structuring network problem solving to deal with consequences oflimited communication-the lack of a global view and the possibility that the individual agents may not have all the information necessary to accurately and completely solve their subproblems-is one of the major focuses of distributed problem-solving research. It is this focus that also is one of the important dis­ tinguishing characteristics of distributed problem-solving research that sets it apart from previous research in AI.

The use of mathematical logic as a formalism for artificial intelligence was recognized by John McCarthy in 1959 in his paper on Programs with Common Sense. In a series of papers in the 1960's he expanded upon these ideas and continues to do so to this date. It is now 41 years since the idea of using a formal mechanism for AI arose. It is therefore appropriate to consider some of the research, applications and implementations that have resulted from this idea. In early 1995 John McCarthy suggested to me that we have a workshop on Logic-Based Artificial Intelligence (LBAI). In June 1999, the Workshop on Logic-Based Artificial Intelligence was held as a consequence of McCarthy's suggestion.

This book presents a series of coordinated studies that explain and illustrate how philosophy must be developed systematically with its problems and topics bound together by links of reciprocal interconnection.

This textbook covers key topics of Elementary Calculus through selected exercises, in a sequence that facilitates development of problem-solving abilities and techniques. It opens with an introduction to fundamental facts of mathematical logic, set theory, and pre-calculus, extending toward functions, limits, derivatives, and integrals. Over 300 solved problems are approached with a simple, direct style, ordered in a way that positively challenges students and helps them build self-confidence as they progress. A special final chapter adds five carefully crafted problems for a comprehensive recap of the work.

This book convenes a selection of 200 mathematical puzzles with original solutions, all celebrating the inquisitive and inspiring spirit of Nobuyuki “Nob” Yoshigahara – a legend in the worldwide community of mathematical and mechanical puzzles. A graduate from the Tokyo Institute of Technology, Yoshigahara invented numerous mechanical puzzles and published over 80 puzzle books. In 2003, he was honored with the Sam Loyd Award, given by the Association for Games & Puzzles International to individuals who have been made a significant contribution to the world of mechanical puzzles.

Dealing with uncertainty, moving from ignorance to knowledge, is the focus of cognitive processes. Understanding these processes and modelling, designing, and building artificial cognitive systems have long been challenging research problems.

Let's assume that you, the reader, have been educated in the basics of science and perhaps some branch of engineering. You have access to textbooks and handbooks, and you are comfortable with a computer. One day, you find yourself faced with a technical problem. If this problem happens to be essentially identical to a previous problem, which you were trained to solve, clearly you will have no difficulty.