Juan Cruz Barsce

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  • 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.

    05/12/2018 ∙ by Jorge A. Palombarini, et al. ∙ 0 share

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  • A Cognitive Approach to Real-time Rescheduling using SOAR-RL

    Ensuring flexible and efficient manufacturing of customized products in an increasing dynamic and turbulent environment without sacrificing cost effectiveness, product quality and on-time delivery has become a key issue for most industrial enterprises. A promising approach to cope with this challenge is the integration of cognitive capabilities in systems and processes with the aim of expanding the knowledge base used to perform managerial and operational tasks. In this work, a novel approach to real-time rescheduling is proposed in order to achieve sustainable improvements in flexibility and adaptability of production systems through the integration of artificial cognitive capabilities, involving perception, reasoning/learning and planning skills. Moreover, an industrial example is discussed where the SOAR cognitive architecture capabilities are integrated in a software prototype, showing that the approach enables the rescheduling system to respond to events in an autonomic way, and to acquire experience through intensive simulation while performing repair tasks.

    05/12/2018 ∙ by Juan Cruz Barsce, et al. ∙ 0 share

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  • Towards Autonomous Reinforcement Learning: Automatic Setting of Hyper-parameters using Bayesian Optimization

    With the increase of machine learning usage by industries and scientific communities in a variety of tasks such as text mining, image recognition and self-driving cars, automatic setting of hyper-parameter in learning algorithms is a key factor for achieving satisfactory performance regardless of user expertise in the inner workings of the techniques and methodologies. In particular, for a reinforcement learning algorithm, the efficiency of an agent learning a control policy in an uncertain environment is heavily dependent on the hyper-parameters used to balance exploration with exploitation. In this work, an autonomous learning framework that integrates Bayesian optimization with Gaussian process regression to optimize the hyper-parameters of a reinforcement learning algorithm, is proposed. Also, a bandits-based approach to achieve a balance between computational costs and decreasing uncertainty about the Q-values, is presented. A gridworld example is used to highlight how hyper-parameter configurations of a learning algorithm (SARSA) are iteratively improved based on two performance functions.

    05/12/2018 ∙ by Juan Cruz Barsce, et al. ∙ 0 share

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