Juval Portugali The notion of complex artificial environments (CAE) refers to theories of c- plexity and self-organization, as well as to artifacts in general, and to artificial - vironments, such as cities, in particular. The link between the two, however, is not trivial. For one thing, the theories of complexity and self-organization originated in the “hard” science and by reference to natural phenomena in physics and bi- ogy. The study of artifacts, per contra, has traditionally been the business of the “soft” disciplines in the humanities and social sciences. The notion of “complex artificial environments” thus implies the supposition that the theories of compl- ity and self-organization, together with the mathematical formalisms and meth- ologies developed for their study, apply beyond the domain of nature. Such a s- st position raises a whole set of questions relating to the nature of 21 century cities and urbanism, to philosophical issues regarding the natural versus the artificial, to the methodological legitimacy of interdisciplinary transfer of theories and me- odologies and to the implications that entail the use of sophisticated, state-of-t- art artifacts such as virtual reality (VR) cities and environments. The three-day workshop on the study of complex artificial environments that took place on the island of San Servolo, Venice, during April 1-3, 2004, was a gathering of scholars engaged in the study of the various aspects of CAE.

Originating from models of biological neural systems, artificial neural networks (ANN) are the cornerstones of artificial intelligence research. Catalyzed by the upsurge in computational power and availability, and made widely accessible with the co-evolution of software, algorithms, and methodologies, artificial neural networks have had a profound

A radical and challenging book which argues that artificial intelligence needs a completely different set of foundations, based on ecological intelligence rather than human intelligence, if it is to deliver on the promise of a better world. This can usher in the greatest transformation in human history, an age of re-integration. Our very existence is dependent upon our context within the Earth System, and so, surely, artificial intelligence must also be grounded within this context, embracing emergence, interconnectedness and real-time feedback. We discover many positive outcomes across the societal, economic and environmental arenas and discuss how this transformation can be delivered. Key Features: Identifies a key weakness in current AI thinking, that threatens any hope of a better world. Highlights the importance of realizing that systems theory is an essential foundation for any technology that hopes to positively transform our world. Emphasizes the need for a radical new approach to AI, based on ecological systems. Explains why ecosystem intelligence, not human intelligence, offers the best framework for AI. Examines how this new approach will impact on the three arenas of society, environment and economics, ushering in a new age of re-integration.

How high-level behaviors arise from low-level rules, and how understanding this relationship can suggest novel solutions to complex real-world problems such as disease prevention, stock-market prediction, and data mining on the Internet. The term "artificial life" describes research into synthetic systems that possess some of the essential properties of life. This interdisciplinary field includes biologists, computer scientists, physicists, chemists, geneticists, and others. Artificial life may be viewed as an attempt to understand high-level behavior from low-level rules -- for example, how the simple interactions between ants and their environment lead to complex trail-following behavior. An understanding of such relationships in particular systems can suggest novel solutions to complex real-world problems such as disease prevention, stock-market prediction, and data mining on the Internet. Since their inception in 1987, the Artificial Life meetings have grown from small workshops to truly international conferences, reflecting the field's increasing appeal to researchers in all areas of science.

The 11th Conference “Artificial Intelligence: Methodology, Systems, Applications – Semantic Web Challenges” (AIMSA 2004) continued successfully pursuing the main aim of the AIMSA series of conferences – to foster the multidisciplinary community of artificial intelligence researchers, embracing both the theoretic underpinnings of the field and the practical issues involved in development, deployment, and maintenance of systems with intelligent behavior. Since the first conference in 1984 AIMSA has provided an ideal forum for international scientific exchange between Central/Eastern Europe and the rest of the world and it is even more important nowadays in the uni- ing Europe. The current AIMSA edition is focused on Semantic Web methods and technologies. The Internet is changing the everyday services landscape, and the way we do things in almost every domain of our life. Web services are rapidly becoming the enabling technology of today's e-business and e-commerce systems, and will soon transform the Web as it is now into a distributed computation and application framework. The emerging Semantic Web paradigm promises to annotate Web artefacts to enable automated reasoning about them. When applied to e-services, the paradigm hopes to provide substantial automation for activities such as discovery, invocation, assembly, and monitoring of e-services. One hundred and seventy-six interesting papers were submitted to the conference.

Artificial Intelligence to Solve Pervasive Internet of Things Issues discusses standards and technologies and wide-ranging technology areas and their applications and challenges, including discussions on architectures, frameworks, applications, best practices, methods and techniques required for integrating AI to resolve IoT issues. Chapters also provide step-by-step measures, practices and solutions to tackle vital decision-making and practical issues affecting IoT technology, including autonomous devices and computerized systems. Such issues range from adopting, mitigating, maintaining, modernizing and protecting AI and IoT infrastructure components such as scalability, sustainability, latency, system decentralization and maintainability. The book enables readers to explore, discover and implement new solutions for integrating AI to solve IoT issues. Resolving these issues will help readers address many real-world applications in areas such as scientific research, healthcare, defense, aeronautics, engineering, social media, and many others. - Discusses intelligent techniques for the implementation of Artificial Intelligence in Internet of Things - Prepared for researchers and specialists who are interested in the use and integration of IoT and Artificial Intelligence technologies

Dr.M.PRIYA, Assistant Professor, Department of Computer Technology and Data Science, Sri Krishna Arts and Science College, Coimbatore, Tamil Nadu, India. Dr.R.VIJAYASHREE, Assistant Professor, Department of Computer Technology and Data Science, Sri Krishna Arts and Science College, Coimbatore, Tamil Nadu, India. Mr.V.J.RAJAKUMAR, Assistant Professor, Department of Computer Technology and Data Science, Sri Krishna Arts & Science College, Coimbatore, Tamil Nadu, India. Mr.S.S.SARAVANA KUMAR, Research Scholar, Department of Computer Science, Sri Krishna Adithya College of Arts and Science, Coimbatore, Tamil Nadu, India.