Distributed Plan Maintenance for Scheduling and Execution

Within the pede project we investigate the generation and execution of plans in distributed environments. Multiple, communicating agents perform tasks like generating process plans and machine schedules and controlling their execution. A general problem in such environments is how to handle unexpected events like high priority jobs and machine breakdowns. A cooperative approach, where agents communicate in order to resolve occurring problems, usually leads to superior overall performance, but is bought at the price of increased network traac and more complex agent architectures. We argue that the application of distributed reason maintenance techniques yields simpler agents and structured communication. We present darms, a distributed reason maintenance system, and show how it can be applied to integrate planning, scheduling, and control. The pede 1 project studies techniques to improve the eecient generation and fail-safe execution of plans in distributed, highly dynamic environments. In a pede domain, we typically have several planning and scheduling agents (planners; e.g. various process planners, a master production scheduler, schedulers for job shops) and a potentially large number of executing agents (executers; for instance humans, machines, robots, and autonomous transport devices). Our example pede problem is a job shop for manufacturing and pre-assembling transmission parts 2. The job shop must perform planning, scheduling and control of execution. It is fed a stream of changes to its pool of jobs. The job shop has a library 1 pede = Planning and Execution in Distributed Environments 2 This example is a modiied version of the case study described in 16]