Designing a Family of Coordination Algorithms

Many researchers have shown that there is no single best organization or coordination mechanism for all environments. This paper discusses the design and implementation of an extendable family of coordination mechanisms, called Generalized Partial Global Planning (GPGP). The set of coordination mechanisms described here assists in scheduling activities for teams of cooperative computational agents. The GPGP approach has several unique features. First, it is not tied to a single domain. Each mechanism is defined as a response to certain features in the current task environment. We show that different combinations of mechanisms are appropriate for different task environments. Secondly, the approach works in conjunction with an agent’s existing local planner/scheduler. Finally, the initial set of five mechanisms presented here generalizes and extends the Partial Global Planning (PGP) algorithm. In comparison to PGP, GPGP schedules tasks with deadlines, it allows agent heterogeneity, it exchanges less global information, and it communicates at multiple levels of abstraction. We analyze the performance of several GPGP algorithm family members and one centralized upper bound reference algorithm, using data from simulations of multiple agent teams working in abstract task environments. We show how to decide if adding a new mechanism is useful, and suggest a way to prune the search for an appropriate combination of mechanisms in an environment. A shorter version of this paper appeared in the Proceedings of the First International Conference on Multi-Agent Systems (ICMAS-95), San Francisco, June 1995. This work was supported by DARPA contract N00014-92-J-1698, Office of Naval Research contract N00014-92-J-1450, and NSF contract IRI-9321324. The content of the information does not necessarily reflect the position or the policy of the Government and no official endorsement should be inferred.