1. Reinforcement learning approach to goal-regulation in a self-evolutionary manufacturing system
- Author
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Shin, Moonsoo, Ryu, Kwangyeol, and Jung, Mooyoung
- Subjects
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REINFORCEMENT learning , *EVOLUTIONARY computation , *MANUFACTURING processes , *FUZZY systems , *INFERENCE (Logic) , *APPROXIMATION theory , *PRODUCTION planning , *SIMULATION methods & models - Abstract
Abstract: Up-to-date market dynamics has been forcing manufacturing systems to adapt quickly and continuously to the ever-changing environment. Self-evolution of manufacturing systems means a continuous process of adapting to the environment on the basis of autonomous goal-formation and goal-oriented dynamic organization. This paper proposes a goal-regulation mechanism that applies a reinforcement learning approach, which is a principal working mechanism for autonomous goal-formation. Individual goals are regulated by a neural network-based fuzzy inference system, namely, a goal-regulation network (GRN) updated by a reinforcement signal from another neural network called goal-evaluation network (GEN). The GEN approximates the compatibility of goals with current environmental situation. In this paper, a production planning problem is also examined by a simulation study in order to validate the proposed goal regulation mechanism. [Copyright &y& Elsevier]
- Published
- 2012
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