Projection and Reaction for Decision Support in Refineries: Combining Multiple Theories

Kurt D. Krebsbach and David J. Musliner

Modern decision support systems are forced to deal with perpetual change in their environment: change that includes a high degree of uncertainty over the current state of the plant, effects of actions, accuracy of sensor readings, and consistency of execution of approved procedures by plant operators. In this paper we report on AEGIS, a large-scale automated system to provide decision support for refinery operations personnel. In particular, we will concentrate on the components concerned with setting goals, planning actions, executing those actions (or suggesting actions to be manually executed), observing action effects, and recovering from unintended plant states. We will describe what worked well, what did not, and why future such attempts must go outside single theories of planning and acting to provide sufficiently flexible decision support in complex environments.

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