Distributed Artificial Intelligence presents a collection of papers describing the state of research in distributed artificial intelligence (DAI). DAI is concerned with the cooperative solution of problems by a decentralized group of agents. The agents may range from simple processing elements to complex entities exhibiting rational behavior. The book is organized into three parts. Part I addresses ways to develop control abstractions that efficiently guide problem-solving; communication abstractions that yield cooperation; and description abstractions that result in effective organizational structure. Part II describes architectures for developing and testing DAI systems. Part III discusses applications of DAI in manufacturing, office automation, and man-machine interactions. This book is intended for researchers, system developers, and students in artificial intelligence and related disciplines. It can also be used as a reference for students and researchers in other disciplines, such as psychology, philosophy, robotics, and distributed computing, who wish to understand the issues of DAI.

Table of Contents

Foreword Part I: Theoretical Issues Chapter 1 Decision Procedures Chapter 2 Cooperation Through Communication in a Distributed Problem Solving Network Chapter 3 Instantiating Descriptions of Organizational Structures Part II: Architectures and Languages Chapter 4 The Architecture of the Agora Environment Chapter 5 MACE: A Flexible Testbed for Distributed AI Research Chapter 6 AF: A Framework for Real-Time Distributed Cooperative Problem Solving Chapter 7 A Connectionist Encoding of Semantic Networks Chapter 8 Semi-Applicative Programming: Examples of Context Free Recognizers Part III: Applications and Examples Chapter 9 DAI for Document Retrieval: The MINDS Project Chapter 10 Manufacturing Experience with the Contract Net Chapter 11 Participant Systems Chapter 12 Distributed Artificial Intelligence: An Annotated Bibliography


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© 1987
Morgan Kaufmann
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