There is a growing belief that the next generation of information processing systems will be based on the paradigm of Intelligent and Cooperative Information Systems (ICISs). Such systems will involve information agents — distributed over the nodes of a common communication network — which work in a synergistic manner by exchanging information and expertise, coordinating their activities and negotiating how to solve parts of a common information-intensive problem. Along with motivating the importance of such systems the paper gives an overview of related research areas, notably Distributed Artificial Intelligence (DAI) and Distributed Databases (DDBs), and presents a generic architecture which views an ICIS as a community of communicating and cooperating intelligent information agents.

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Knowledge-based System (KBS) technologies have been applied to a variety of knowledge-related tasks with varying degrees of success. Differentiating among classes of knowledge-related tasks, based on the amounts of problem-solving knowledge and case-specific data involved, can provide valuable insight into why this occurs. Based on this comparison, four classes of problems are described. One class, of data-intensive tasks, includes problem types that are difficult or impossible for humans to perform, yet may be solved in a cost-effective manner using currently accessible KBS technology. The characteristic features of problems in this class are given, together with an example of a successfully fielded knowledge-based system that solves a problem from this class.

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Many large, widely distributed organizations struggle with the enormous task of providing the right information to the right people at the right time. Organizations facing this task often develop groups of analysts who specialize in supplying information transport and access capabilities to end-users. However, this approach has several drawbacks. Our aim is to address these problems at their source -not by replacing analysts in the information access problem, but by automating the roles assumed by analysts. Toward this end, this paper describes a strategy that combines four techniques to solve such problems: (1) an architecture for coarse-grained agents (CGAS}, (2) a communication protocol that enables CGAS to interact, (3,) an intermediate query language (IQL), designed around user-level concepts, and (4) a query translation mechanism that transforms IQL requests into database-specific queries. A prototype implementation, known as oMIE, is described.