In the early days of artificial intelligence it was widely believed that powerful computers would, in the future, enable mankind to solve many real-world problems through the use of very general inference procedures and very little domain-specific knowledge. With the benefit of hindsight, this view can now be called quite naive. The field of expert systems, which developed during the early 1970s, embraced the paradigm that Knowledge is Power - even very fast computers require very large amounts of very specific knowledge to solve non-trivial problems. Thus, the field of large knowledge bases has emerged.

Chapter one presents the Cyc "philosophy" or paradigm. Chapter 2 presents a global overview of Cyc, including its representation language, the ontology f its knowledge base, and teh environment which it functions. Chapter 3 goes into much more detail on the representation language, including the structure and function of Cyc's metalevel agenda mechanism. Chapter 4 presents heuristics for ontological engineering, the pricnples upon whcihc Cyc's ontology is based. Chapter 5 the provides a glimpse into the global ontology of knowledge. Chapter 6 explains how we "solve" (i.e., adequately handle) the various tough representation thorns (substances, time, space, structures, composite mental/physical objects, beliefs, uncertainty, etc. ). Chapter 7 surveys the mistakes that new knowledge tnereres most often commit. Chapter 8, the concluding chapter, includes a brief status report on the project, and a statement of goals and a timetable for the coming five years.

The 12th International Symposium on Distributed Computing and Artificial Intelligence 2015 (DCAI 2015) is a forum to present applications of innovative techniques for studying and solving complex problems. The exchange of ideas between scientists and technicians from both the academic and industrial sector is essential to facilitate the development of systems that can meet the ever-increasing demands of today’s society. The present edition brings together past experience, current work and promising future trends associated with distributed computing, artificial intelligence and their application in order to provide efficient solutions to real problems. This symposium is organized by the Osaka Institute of Technology, Qatar University and the University of Salamanca.

"If anyone knows anything about the web, where it's been and where it's going, it's David Weinberger. . . . Too Big To Know is an optimistic, if not somewhat cautionary tale, of the information explosion." -- Steven Rosenbaum, Forbes With the advent of the Internet and the limitless information it contains, we're less sure about what we know, who knows what, or even what it means to know at all. And yet, human knowledge has recently grown in previously unimaginable ways and in inconceivable directions. In Too Big to Know, David Weinberger explains that, rather than a systemic collapse, the Internet era represents a fundamental change in the methods we have for understanding the world around us. With examples from history, politics, business, philosophy, and science, Too Big to Know describes how the very foundations of knowledge have been overturned, and what this revolution means for our future.

This edited book presents scientific results of the 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2016) which was held on May 30 - June 1, 2016 in Shanghai, China. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. Research results about all aspects (theory, applications and tools) of computer and information science, and to discuss the practical challenges encountered along the way and the solutions adopted to solve them.

Provides relevant theoretical frameworks, latest empirical research findings, and practitioners' best practices in the area of organizational memory.