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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.
Distributed Coordination of Multi-agent Networks introduces problems, models, and issues such as collective periodic motion coordination, collective tracking with a dynamic leader, and containment control with multiple leaders, and explores ideas for their solution. Solving these problems extends the existing application domains of multi-agent networks; for example, collective periodic motion coordination is appropriate for applications involving repetitive movements, collective tracking guarantees tracking of a dynamic leader by multiple followers in the presence of reduced interaction and partial measurements, and containment control enables maneuvering of multiple followers by multiple leaders.
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.
Distributed Intelligent Systems: A Coordination Perspective 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; 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. 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
This book is concerned with Artificial Intelligence (AI) concepts and techniques as applied to industrial decision making, control and automation problems. The field of AI has been expanded enormously during the last years due to that solid theoretical and application results have accumulated. During the first stage of AI development most workers in the field were content with illustrations showing ideas at work on simple problems. Later, as the field matured, emphasis was turned to demonstrations that showed the capability of AI techniques to handle problems of practical value. Now, we arrived at the stage where researchers and practitioners are actually building AI systems that face real-world and industrial problems. This volume provides a set of twenty four well-selected contributions that deal with the application of AI to such real-life and industrial problems. These contributions are grouped and presented in five parts as follows: Part 1: General Issues Part 2: Intelligent Systems Part 3: Neural Networks in Modelling, Control and Scheduling Part 4: System Diagnostics Part 5: Industrial Robotic, Manufacturing and Organizational Systems Part 1 involves four chapters providing background material and dealing with general issues such as the conceptual integration of qualitative and quantitative models, the treatment of timing problems at system integration, and the investigation of correct reasoning in interactive man-robot systems.
This is the first international conference aimed at bringing the distributed database and distributed AI (DAD experts together, from both academia and industry, in order to discuss the issues of the next generation of knowledge based systems, namely Cooperating Knowledge Based Systems or CKBS for short. As the area of CKBS is new, we intended it to be an ideas conference - a conference where interesting new ideas, rather than results from completed projects, are explored, discussed, and debated. The conference was organised by the DAKE Centre. This is an interdisciplinary centre at the University of Keele for research and development in Data and Knowledge Engineering (DAKE). The Centre draws most of its strength from the Department of Computer Science which also provides administrative support for the activities of the Centre, although its membership is spread over several departments. The Centre has three main streams of research activities, namely: Large Knowledge Bases Software Engineering Neural Networks The Large Knowledge Base group, which provided the focus for this conference, is active in a number of research areas relating to data and knowledge bases, spanning from distributed databases to cooperations among data and knowledge bases. The current research topics include integration of data and knowledge bases and coopera ting knowledge based systems, with several major projects in the latter (see the entries under the Poster Session given below).
This book presents the proceedings of the 1st International Symposium on Intelligent and Distributed Computing, IDC 2007, held in Craiova, Romania, October 2007. Coverage includes: autonomous and adaptive computing; data mining and knowledge discovery; distributed problem solving and decision making; e-business, e-health and e-learning; genetic algorithms; image processing; information retrieval; intelligence in mobile and ubiquitous computing.
Distributed Artificial Intelligence
This volume contains the 5 invited papers and 72 selected papers that were presented at the Fifth International Conference on Industrial and Engineering Applications of Artificial Intelligence. This is the first IEA/AIE conference to take place outside the USA: more than 120 papers were received from 23 countries, clearly indicating the international character of the conference series. Each paper was reviewed by at least three referees. The papers are grouped into parts on: CAM, reasoning and modelling, pattern recognition, software engineering and AI/ES, CAD, vision, verification and validation, neural networks, machine learning, fuzzy logic and control, robotics, design and architecture, configuration, finance, knowledge-based systems, knowledge representation, knowledge acquisition and language processing, reasoning and decision support, intelligent interfaces/DB and tutoring, fault diagnosis, planning and scheduling, and data/sensor fusion.
This book looks at the process of human cognition and the way complex problems are solved by decomposing them into a list of strategic objectives, before focusing individually on each objective to plan for a tactical solution. This process has been formulated by military planners in the form of the Standard Operating Procedure, by which problem solving is organised into four different stages: deliberation, planning, war meeting and plan execution. This has enabled the development of a methodology for problem solving in a dynamic environment. This is illustrated with the help of a six-case study in chess and prediction of exchange rate movement in a foreign exchange market.