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Artificial Intelligence presents a practical guide to AI, including agents, machine learning and problem-solving simple and complex domains.
"This book is a catalyst for emerging research in intelligent information, specifically artificial intelligent technologies and applications to assist in improving productivity in many roles such as assistants to human operators and autonomous decision-making components of complex systems"--Provided by publisher.
This is the first comprehensive introduction to multiagent systems and contemporary distributed artificial intelligence that is suitable as a textbook.
This monograph presents a comprehensive state-of-the-art survey on approaches to the design of intelligent agents. On the theoretical side, the author identifies a set of general requirements for autonomous interacting agents and provides an essential step towards understanding the principles of intelligent agents. On the practical side, the novel agent architecture InteRRaP is introduced: the detailed description and evaluation of this architecture is an ideal guideline and case study for software engineers or researchers faced with the task of building an agent system. The book uniquely bridges the gap between theory and practice; it addresses active and novice researchers as well as practitioners interested in applicable agent technology.
In this book, Jacques Ferber has brought together all the recent developments in the field of multi-agent systems - an area that has seen increasing interest and major developments over the last few years. The author draws on work carried out in various disciplines, including information technology, sociology and cognitive psychology to provide a coherent and instructive picture of the current state-of-the-art. The book introduces and defines the fundamental concepts that need to be understood, clearly describes the work that has been done, and invites readers to reflect upon the possibilities of the future.
A comprehensive introduction to new approaches in artificial intelligence and robotics that are inspired by self-organizing biological processes and structures. New approaches to artificial intelligence spring from the idea that intelligence emerges as much from cells, bodies, and societies as it does from evolution, development, and learning. Traditionally, artificial intelligence has been concerned with reproducing the abilities of human brains; newer approaches take inspiration from a wider range of biological structures that that are capable of autonomous self-organization. Examples of these new approaches include evolutionary computation and evolutionary electronics, artificial neural networks, immune systems, biorobotics, and swarm intelligence—to mention only a few. This book offers a comprehensive introduction to the emerging field of biologically inspired artificial intelligence that can be used as an upper-level text or as a reference for researchers. Each chapter presents computational approaches inspired by a different biological system; each begins with background information about the biological system and then proceeds to develop computational models that make use of biological concepts. The chapters cover evolutionary computation and electronics; cellular systems; neural systems, including neuromorphic engineering; developmental systems; immune systems; behavioral systems—including several approaches to robotics, including behavior-based, bio-mimetic, epigenetic, and evolutionary robots; and collective systems, including swarm robotics as well as cooperative and competitive co-evolving systems. Chapters end with a concluding overview and suggested reading.
The first book to provide an integrative presentation of the issues, challenges and success of designing, building and using agent applications. The chapters presented are written by internationally leading authorities in the field, with a general audience in mind. The result is a unique overview of agent technology applications, ranging from an introduction to the technical foundations to reports on dealing with specific agent systems in practice.
Advances in narrow artificial intelligence make possible agentive systems that do things directly for their users (like, say, an automatic pet feeder). They deliver on the promise of user-centered design, but present fresh challenges in understanding their unique promises and pitfalls. Designing Agentive Technology provides both a conceptual grounding and practical advice to unlock agentive technology’s massive potential.
Intelligent agents are employed as the central characters in this introductory text. Beginning with elementary reactive agents, Nilsson gradually increases their cognitive horsepower to illustrate the most important and lasting ideas in AI. Neural networks, genetic programming, computer vision, heuristic search, knowledge representation and reasoning, Bayes networks, planning, and language understanding are each revealed through the growing capabilities of these agents. A distinguishing feature of this text is in its evolutionary approach to the study of AI. This book provides a refreshing and motivating synthesis of the field by one of AI's master expositors and leading researches. - An evolutionary approach provides a unifying theme - Thorough coverage of important AI ideas, old and new - Frequent use of examples and illustrative diagrams - Extensive coverage of machine learning methods throughout the text - Citations to over 500 references - Comprehensive index
Most artificial intelligence research investigates intelligent behavior for a single agent--solving problems heuristically, understanding natural language, and so on. Distributed Artificial Intelligence (DAI) is concerned with coordinated intelligent behavior: intelligent agents coordinating their knowledge, skills, and plans to act or solve problems, working toward a single goal, or toward separate, individual goals that interact. DAI provides intellectual insights about organization, interaction, and problem solving among intelligent agents. This comprehensive collection of articles shows the breadth and depth of DAI research. The selected information is relevant to emerging DAI technologies as well as to practical problems in artificial intelligence, distributed computing systems, and human-computer interaction. "Readings in Distributed Artificial Intelligence" proposes a framework for understanding the problems and possibilities of DAI. It divides the study into three realms: the natural systems approach (emulating strategies and representations people use to coordinate their activities), the engineering/science perspective (building automated, coordinated problem solvers for specific applications), and a third, hybrid approach that is useful in analyzing and developing mixed collections of machines and human agents working together. The editors introduce the volume with an important survey of the motivations, research, and results of work in DAI. This historical and conceptual overview combines with chapter introductions to guide the reader through this fascinating field. A unique and extensive bibliography is also provided.