Generating Rescheduling Knowledge using Reinforcement Learning in a Cognitive Architecture

TL;DR

This paper presents a novel method combining reinforcement learning with cognitive architecture to generate adaptive rescheduling knowledge for manufacturing systems, enhancing flexibility and autonomous decision-making.

Contribution

It introduces a new approach integrating reinforcement learning with the Soar cognitive architecture to produce dynamic, logical rescheduling rules for real-time manufacturing adjustments.

Findings

Enables autonomous assessment of operational range.

Facilitates experience acquisition through simulation.

Improves reactive rescheduling capabilities.

Abstract

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.