Knowledge-based multiagent coordination

In many domains, intelligent agents must coordinate their activities in ord er for them to be successful both individually and collectively. Over the l ast ten years, research in distributed artificial intelligence has emphasiz ed building knowledge-lean systems, where coordination emerges either from simple rules of behavior or from a deep understanding of general coordinati on strategies. In this paper, we contend that there is an alternative for d omains in which the types and methods of coordination are well structured ( even though the environment may be very unstructured and dynamic). The alte rnative is to build real-time, knowledge-based agents that have a broad-but shallow-understanding of how to coordinate. We demonstrate the viability o f this approach by example. Specifically, we have built agents that model t he coordination performed by Navy and Air Force pilots and controllers in a ir-to-air and air-to-ground missions within a distributed interactive simul ation environment. The major contribution of the paper is an examination of the requirements and approaches for supporting knowledge-based coordinatio n, in terms of the structure of the domain, the agents' knowledge of the do main, and the underlying AI architecture.