Exploiting Meta-Level Information in a Distributed Scheduling System

Distributed AI

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 12

Collaboration

In this paper, we study the problem of achieving efficient interaction in a distributed scheduling system whose scheduling agents may borrow resources from one another. Specifically, we expand on Sycara’s use of resource texture measures in a distributed scheduling system with a central resource monitor for each resource type and apply it to the decentralized case. We show how analysis of the abstracted resource requirements of remote agents can guide an agent’s choice of local scheduling activities not only in determining local constraint tightness, but also in identifying activities that reduce global uncertainty. We also exploit meta-level information to allow the scheduling agents to make reasoned decisions about when to attempt to solve impasses locally through backtracking and constraint relaxation and when to request resources from remote agents. Finally, we describe the current state of negotiation in our system and discuss plans for integrating a more sophisticated cost model into the negotiation protocol. This work is presented in the context of the Distributed Airport Resource Management System, a multi-agent system for solving airport ground service scheduling problems.