From: AAAI Technical Report SS-99-06. Compilation copyright © 1999, AAAI (www.aaai.org). All rights reserved. Adjustable Autonomy for Manufacturing Cell (Extended Abstract) Control Systems Wei Zhang The Boeing Company P.O. Box 3707, MS7L-66 Seattle, WA98124-2207 Manufacturing-process control and management often deal with millions of activities. These activities are defined at several levels of abstraction from the machine level concerning specific machining activities to the cell/line level that supports interaction of a group of machines, and more generally to the shop-floor level and then to the supply-chain level. Cell control--which particularly focuses on developing autonomous systems for supporting interactive activities at the cell level--is often most crucial in production. Cell control normally must consider activities and their relationship across manyof the levels. The goal of cell control is factory automation so that a process can be run autonomously to achieve maximal machine efficiency (we assume this is done under assured product quality). While this goal is highly desired, in practice a number of factors must be closely examined. Firstly, a system must be developed to have flexible functionalities to support the leverage between autonomous machine control and human intervention. Secondly, a system must be maintainable to support current processes and evolution of the processes that may happen in the future. Thirdly, a system must be developed in a cost-effective manner so as to satisfy financial requirements and ensure investment return (Full autonomy often may be too expensive to afford). Therefore, often a study on the level of autonomy is very important. This working note focuses on the study of adjustable autonomy for cell control. This means that we are not studying how to determine the level of autonomy needed to be applied in a fixed manner in a given environment. Rather, we are studying how to develop a system with flexible functionalities so that the level of autonomy can be adjusted dynamically during process execution. This study applies the agent-based methodology. We apply the multi-agent paradigm to represent interactive activities amongvarious functional components of cell control performing machining control, scheduling and planning, sensing and monitoring, diagnosis and recovery, and simulations et al. Similar functional components have been studied in Dorais et al’s investigation for adjustable autonomy for human-centered au- 136 tonomous systems on Mars (Dorais et al. 1998). Wediscuss on-going research at Boeing Applied Research & Technology in developing adjustable autonomymechanisms for cell control. One area of research is to investigate the representation problem. Weformulate the multi-agent problem as a global optimization problem and each single agent needs to know how to communicate with other agents to help achieve global optimality. Another area of research is to develop reinforcement learning methods based on this optimization framework to learn to adjust the level of autonomydynamically. Reinforcement learning has been successfully applied in many complicated, large real-word problems to learn to act optimally in a dynamic environment (Kaelbling et al. 1996, Zhang and Dietterich 1995). References G. Dorais, R.P. Bonasso, D. Kortenkamp, B. Pell, and D. Schreckenghost. Adjustable autonomy for humancentered autonomous systems on Mars. In Mars Society Conference, 1998. L. P. Kaelbling, M. L. Littman, and A. W. Moore. Reinforcement learning: a survey. Journal of AI Research, 4, 1996. W. Zhang and T. Dietterich. A reinforcement learning approach to job-shop scheduling. In IJCAI-95, pages 1114-1120, 1995.