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Complex applications in artificial intelligence need a multiple representation of knowledge and tasks in terms of abstraction levels and points of view. The integration of numerous resources (knowledge-based systems, real-time systems, data bases, etc.), often geographically distributed on different machines connected into a network, is moreover a necessity for the development of real scale systems. The distributed artificial intelligence (DAI) approach is thus becoming important to solve problems in complex situations. There are several currents in DAI research and we are involved in the design of DAI programming platforms for large and complex real-world problem solving systems. Blackboard systems constitute the earlier architecture. It is based on a shared memory which permits the communication among a collection of specialists and an external and unique control structure. Blackboard architectures have been extended, especially to introduce parallelism. Multi-agent architectures are based on coordinated agents (problem-solvers) communicating most of the time via message passing. A solution is found through the cooperation between several agents, each of them being in charge of a specific task, but no one having sufficient resources to obtain a solution. Coordination, cooperation, knowledge, goal, plan, exchanges are then necessary to reach a global solution. Our own research is along this last line. The current presentation describes Multi-Agent Problem Solver (MAPS) which is an agent-oriented language for a DAI system design embedded in a full programming environment. An agent is conceived as an autonomous entity with specific goals, roles, skills, and resources. Knowledge (descriptive and operative) is distributed among agents organized into networks (agents communicate through message sending). Agents are moreover geographically distributed and run in a parallel mode. Our purpose is to build a powerful environment for DAI applications design that not only solve large problems, but also help in the formulation, description, and decomposition of a problem in terms of groups of intelligent agents. Several applications have been developed with MAPS in Computer Vision (KISS system), biomedical diagnosis (KIDS system), and speech understanding. The KISS system is presented to illustrate MAPS potentialities.
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