MULTIAGENT COORDINATION AND COOPERATION IN A DISTRIBUTED DYNAMIC ENVIRONMENT WITH LIMITED RESOURCES

Coordination and cooperation are two major concerns in distributed artificial intelligence (DAI) systems. How can a group of geographically distributed agents properly allocate a set of tasks among themselves while satisfying different types of constraints? Also, in an environment of limited resources, how can agents resolve resource conflicts and effectively accomplish tasks? We have examined these two problems and have developed new techniques to promote multiagent coordination and cooperation. A novel method of negotiation allows agents to bid for tasks based upon the agents' capabilities. Furthermore, the use of a threshold value ensures that only the best agents for a task become task commanders and renders some tasks to be renegotiated as agents improve their bids. To resolve resource conflicts, a technique called 'hierarchical iterative conflict resolution' has been implemented. This allows conflicts to be resolved in an iterative manner, based upon a hierarchy of task priorities. Agents with higher priority tasks have the authority to borrow resources from agents with lower priority tasks. It ensures that higher priority tasks will be solved before those of lower priority. These new techniques were employed in a DAI testbed which simulates an air war environment.