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More and more, software systems involve autonomous and distributed software components that have to execute and interact in open and dynamic environments, such as in pervasive, autonomous, and mobile applications. The requirements with respect to dynamics, openness, scalability, and decentralization call for new approaches to software design and development, capable of supporting spontaneous configuration, tolerating partial failures, or arranging adaptive reorganization of the whole system. Inspired by the behaviour of complex natural systems, scientists and engineers have started to adjust their mechanisms and techniques for self-organization and adaption to changing environments. In line with these considerations, Mamei and Zambonelli propose an interaction model inspired by the way masses and particles in our universe move and self-organize according to contextual information represented by gravitational and electromagnetic fields. The key idea is to have the components’ actions driven by computational force fields, generated by the components themselves or by some infrastructures, and propagated across the environment. Together with its supporting middleware infrastructure – available with additional information under http://www.agentgroup.unimore.it – this model can serve as the basis for a general purpose and widely applicable approach for the design and development of adaptive distributed applications.

Evolutionary programming (Computer science) --- Adaptive computing systems --- Distributed artificial intelligence. --- Embedded computer systems --- Design. --- Programming. --- DAI (Artificial intelligence) --- Distributed AI (Artificial intelligence) --- Artificial intelligence --- Adaptive computing --- Configurable computing systems --- Reconfigurable computing systems --- Computer systems --- Computer programming --- Information Technology --- Artificial Intelligence --- Artificial intelligence. --- Computer Communication Networks. --- Software engineering. --- Artificial Intelligence. --- Software Engineering. --- Computer software engineering --- Engineering --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Computer communication systems. --- Communication systems, Computer --- Computer communication systems --- Data networks, Computer --- ECNs (Electronic communication networks) --- Electronic communication networks --- Networks, Computer --- Teleprocessing networks --- Data transmission systems --- Digital communications --- Electronic systems --- Information networks --- Telecommunication --- Cyberinfrastructure --- Network computers --- Distributed processing

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The complexity of systems studied in distributed artificial intelligence (DAI), such as multi-agent systems, often makes it extremely difficult or even impossible to correctly and completely specify their behavioral repertoires and dynamics. There is broad agreement that such systems should be equipped with the ability to learn in order to improve their future performance autonomously. The interdisciplinary cooperation of researchers from DAI and machine learning (ML) has established a new and very active area of research and development enjoying steadily increasing attention from both communities. This state-of-the-art report documents current and ongoing developments in the area of learning in DAI systems. It is indispensable reading for anybody active in the area and will serve as a valuable source of information.

Machine learning --- Distributed artificial intelligence --- Computer Science --- Engineering & Applied Sciences --- 681.3*I2 <063> --- Learning, Machine --- Artificial intelligence --- Machine theory --- DAI (Artificial intelligence) --- Distributed AI (Artificial intelligence) --- Artificial intelligence. AI--Congressen --- 681.3*I2 <063> Artificial intelligence. AI--Congressen --- Computer science. --- Programming languages (Electronic computers). --- Artificial intelligence. --- Computer simulation. --- Computer Science. --- Artificial Intelligence (incl. Robotics). --- Simulation and Modeling. --- Programming Languages, Compilers, Interpreters. --- Computer modeling --- Computer models --- Modeling, Computer --- Models, Computer --- Simulation, Computer --- Electromechanical analogies --- Mathematical models --- Simulation methods --- Model-integrated computing --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Self-organizing systems --- Fifth generation computers --- Neural computers --- Computer languages --- Computer program languages --- Computer programming languages --- Machine language --- Languages, Artificial --- Informatics --- Science --- Congresses --- Artificial Intelligence. --- Machine learning - Congresses --- Distributed artificial intelligence - Congresses

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Distributed Intelligent Systems: A Coordination Perspective addresses and comprehensively answers commonly asked questions about coordination in agent-oriented distributed systems. Characterizing the state-of-the-art research in the field of coordination with regard to the development of distributed agent-oriented systems is a particularly complex endeavour, as the space of available approaches is indeed considerable, and research is independently conducted in a great number of domains. While existing books deal with specific aspects of coordination, the major contribution of this book lies in the attempt to provide an in-depth review covering a wide range of issues regarding multi-agent coordination in Distributed Artificial Intelligence. In addition to reporting various sources of confusion, this book outlines the existence of a plethora of strategies and techniques adapted to different problems, agent-oriented systems, environments, and domains. In short, the current book identifies the absence of a single unified approach in addressing multi-agent coordination problems arising in any system or organization. This book, written by world-class leaders in this field, dedicates itself to providing a state-of-the-art review of current coordination strategies and techniques. The book also describes a broad range of application domains which implement many of the coordination strategies and techniques from the field of multi-agent systems. The application domains include defense, transportation, health care, telecommunication and e-business. Based on current practical deployed applications and existing capabilities, this book also identifies and thoroughly examines trends, challenges, and future agent-oriented research directions. Key features: Unveils the lack of coherence and order that characterizes the area of research pertaining to coordination of distributed intelligent systems Examines coordination models, frameworks, strategies and techniques to enable the development of distributed intelligent agent-oriented systems. Provides specific recommendations to realize more widespread deployment of agent-based systems Distributed Intelligent Systems: A Coordination Perspective is designed for a professional audience composed of practitioners and researchers in industry. This book is also suitable as a reference or secondary textbook for advanced-level students in computer science and engineering.

Distributed artificial intelligence. --- Intelligent agents (Computer software). --- Distributed artificial intelligence --- Intelligent agents (Computer software) --- Computer Science --- Mechanical Engineering - General --- Engineering & Applied Sciences --- Mechanical Engineering --- Information Technology --- Artificial Intelligence --- Intelligent control systems. --- Intelligent control --- Intelligent controllers --- DAI (Artificial intelligence) --- Distributed AI (Artificial intelligence) --- Computer science. --- Computer organization. --- Computer communication systems. --- Operating systems (Computers). --- Computers. --- Artificial intelligence. --- Computer Science. --- Artificial Intelligence (incl. Robotics). --- Computer Communication Networks. --- Operating Systems. --- Computer Systems Organization and Communication Networks. --- Information Systems and Communication Service. --- Automatic control --- Artificial intelligence --- Computer network architectures. --- Information systems. --- Artificial Intelligence. --- Architectures, Computer network --- Network architectures, Computer --- Computer architecture --- Computer operating systems --- Computers --- Disk operating systems --- Systems software --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Operating systems

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Civil engineering structures such as buildings, bridges, stadiums, and offshore structures play an import role in our daily life. However, constructing these structures requires lots of budget. Thus, how to cost-efficiently design structures satisfying all required design constraints is an important factor to structural engineers. Traditionally, mathematical gradient-based optimal techniques have been applied to the design of optimal structures. While, many practical engineering optimal problems are very complex and hard to solve by traditional method. In the past few decades, swarm intelligence algorithms, which were inspired by the social behaviour of natural animals such as fish schooling and bird flocking, were developed  because they do not require conventional mathematical assumptions and thus possess better global search abilities than the traditional optimization algorithms and  have attracted more and more attention. These intelligent based algorithms are very suitable for continuous and discrete design variable problems such as ready-made structural members and have been vigorously applied to various structural design problems and obtained good results. This book gathers the authors’ latest research work related with particle swarm optimizer algorithm and group search optimizer algorithm as well as their application to structural optimal design. The readers can understand the full spectrum of the algorithms and apply the algorithms to their own research problems.  .

Fluid dynamics -- Mathematics. --- Mathematical optimization. --- Shape theory (Topology). --- Engineering & Applied Sciences --- Computer Science --- Distributed artificial intelligence. --- Swarm intelligence. --- Collective intelligence --- DAI (Artificial intelligence) --- Distributed AI (Artificial intelligence) --- Engineering. --- Artificial intelligence. --- Computational intelligence. --- Structural mechanics. --- Civil engineering. --- Computational Intelligence. --- Artificial Intelligence (incl. Robotics). --- Civil Engineering. --- Structural Mechanics. --- Engineering --- Public works --- Architectural engineering --- Engineering, Architectural --- Structural mechanics --- Structures, Theory of --- Structural engineering --- Intelligence, Computational --- Artificial intelligence --- Soft computing --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Construction --- Industrial arts --- Technology --- Cellular automata --- Distributed artificial intelligence --- Mechanics. --- Mechanics, Applied. --- Artificial Intelligence. --- Solid Mechanics. --- Applied mechanics --- Engineering, Mechanical --- Engineering mathematics --- Classical mechanics --- Newtonian mechanics --- Physics --- Dynamics --- Quantum theory

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Intelligent agents (Computer software) --- Distributed artificial intelligence. --- Computer simulation. --- Application software --- Agents intelligents (Logiciels) --- Intelligence artificielle répartie --- Simulation par ordinateur --- Logiciels d'application --- Development. --- Développement --- Computer simulation --- Distributed artificial intelligence --- Agents, Autonomous (Computer software) --- Agents, Cognitive (Computer software) --- Agents, Intelligent (Computer software) --- Assistants, Cognitive (Computer software) --- Assistants, Intelligent software --- Autonomous agents (Computer software) --- Cognitive agents (Computer software) --- Cognitive assistants (Computer software) --- IAs (Computer software) --- Intelligent agent software --- Intelligent software agents --- Intelligent software assistants --- Software agents (Computer software) --- Special agents (Computer software) --- Artificial intelligence --- Development of application software --- DAI (Artificial intelligence) --- Distributed AI (Artificial intelligence) --- Computer modeling --- Computer models --- Modeling, Computer --- Models, Computer --- Simulation, Computer --- Electromechanical analogies --- Mathematical models --- Simulation methods --- Model-integrated computing --- Development --- Computer programs