Generating Rescheduling Knowledge using Reinforcement Learning in a
  Cognitive Architecture
  In order to reach higher degrees of flexibility, adaptability and autonomy in
manufacturing systems, it is essential to develop new rescheduling
methodologies which resort to cognitive capabilities, similar to those found in
human beings. Artificial cognition is important for designing planning and
control systems that generate and represent knowledge about heuristics for
repair-based scheduling. Rescheduling knowledge in the form of decision rules
is used to deal with unforeseen events and disturbances reactively in real
time, and take advantage of the ability to act interactively with the user to
counteract the effects of disruptions. In this work, to achieve the
aforementioned goals, a novel approach to generate rescheduling knowledge in
the form of dynamic first-order logical rules is proposed. The proposed
approach is based on the integration of reinforcement learning with artificial
cognitive capabilities involving perception and reasoning/learning skills
embedded in the Soar cognitive architecture. An industrial example is discussed
showing that the approach enables the scheduling system to assess its
operational range in an autonomic way, and to acquire experience through
intensive simulation while performing repair tasks.
