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.