This survey characterizes an emerging research area, sometimes called coordination theory, that focuses on the interdisciplinary study of coordination. Research in this area uses and extends ideas about coordination from disciplines such as computer science, organization theory, operations research, economics, linguistics, and psychology.A key insight of the framework presented here is that coordination can be seen as the process of managing dependencies among activities. Further progress, therefore, should be possible by characterizing different kinds of dependencies and identifying the coordination processes that can be used to manage them. A variety of processes are analyzed from this perspective, and commonalities across disciplines are identified. Processes analyzed include those for managing shared resources, producer/consumer relationships, simultaneity constraints, and task/subtask dependencies.Section 3 summarizes ways of applying a coordination perspective in three different domains:(1) understanding the effects of information technology on human organizations and markets, (2) designing cooperative work tools, and (3) designing distributed and parallel computer systems. In the final section, elements of a research agenda in this new area are briefly outlined.

/pdf/the-interdisciplinary-study-of-coordination-22jsxamy1h.pdf

The interdisciplinary study of coordination

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.

Readings in Distributed Artificial Intelligence

A system and method for data communication connecting on-line networks with on-line and off-line computers. The present system provides for broadcast of up to the minute notification centric information thereby providing an instant call to action for users who are provided with the ability to instantaneously retrieve further detailed information. The notification centric portions of information is wirelessly broadcast to wireless receiving devices which are attached to computing devices. Upon receipt of the information at the personal computer, the user is notified through different multimedia alerts that there is an incoming message. Wirelessly broadcasted URL's, associated with the data, are embedded in data packets and provide an automated wired or wireless connection back to the information source for obtaining detailed data.

System and Method for Transmission of Data

The evolution of control architectures for automated manufacturing systems

A dynamic load balancing method is proposed for a class of large-diameter multiprocessor systems. The method is based on the "gradient model," which entails transferring backlogged tasks to nearby idle processors according to a pressure gradient indirectly established by requests from idle processors. The algorithm is fully distributed and asynchronous. Global balance is achieved by successive refinements of many localized balances. The gradient model is formulated so as to be independent of system topology.

/pdf/the-gradient-model-load-balancing-method-2buina021a.pdf

The Gradient Model Load Balancing Method

Distributed computer systems have been the subject of a vast amount of research. Many prototype distributed computer systems have been built at university, industrial, commercial, and government research laboratories, and production systems of all sizes and types have proliferated. It is impossible to survey all distributed computing system research. Instead, this paper identifies six fundamental distributed computer system research issues, points out open research problems in these areas, and describes how these six issues and solutions to problems associated with them transect the communications subnet, the distributed operating system, and the distributed database areas. It is intended that this perspective on distributed computer system research serve as a form of survey, but more importantly to illustrate and encourage a better integration and exchange of ideas from various subareas of distributed computer system research.

A Perspective on Distributed Computer Systems

A successful technique for improving system performance has been to migrate often used software functions into firmware. An extension of this technique, called vertical migration, has been developed that deals with migrations of functions between any two levels in a multilevel interpretive hierarchy. In both of these approaches the effect of multiple migrations on system performance is not fully understood and has not been formally analyzed. In this paper the effects of multiple migrations on system performance are formally described. Three distinct types of migration are identified and formulas for the execution time savings of each type are derived.

The Types and Interactions of Vertical Migrations of Functions in a Multilevel Interpretive System

THE research domains of parallel and distributed computing have a significant overlap. With the advent of general-purpose multiprocessors, this overlap is bound to increase. This Special Issue attempts to draw together several papers from both of these separate research domains to illustrate commonalty and to encourage greater interaction among researchers in the two communities.

Introduction&#8212;Parallel and Distributed Computing

There is a wide spectrum of techniques that can be aptly named decentralized control. However, certain functions in distributed operating systems, e.g., scheduling, operate under such demanding requirements that no known optimal control solutions exist. It has been shown that heuristics are necessary. This paper presents a heuristic for the effective cooperation of multiple decentralized components of a job scheduling function. An especially useful feature of the heuristic is that it can dynamically adapt to the quality of the state information being processed. Extensive simulation results show the utility of this heuristic. The simulation results are compared to several analytical models and a baseline simulation model. The heuristic itself is based on the application of Bayesian decision theory. Bayesian decision theory was used because its principles can be applied as a systematic approach to complex decision making under conditions of imperfect knowledge, and it can run relatively cheaply in real time.

An Application of Bayesian Decision Theory to Decentralized Control of Job Scheduling

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A Perspective on Distributed Computer Systems

The Types and Interactions of Vertical Migrations of Functions in a Multilevel Interpretive System

Introduction—Parallel and Distributed Computing

An Application of Bayesian Decision Theory to Decentralized Control of Job Scheduling

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Papers

A Perspective on Distributed Computer Systems

The Types and Interactions of Vertical Migrations of Functions in a Multilevel Interpretive System

Introduction&#8212;Parallel and Distributed Computing

An Application of Bayesian Decision Theory to Decentralized Control of Job Scheduling

Introduction—Parallel and Distributed Computing