Reinforcement learning approach to goal-regulation in a self-evolutionary manufacturing system

• Published in: Expert systems with applications Vol. 39; no. 10; pp. 8736 - 8743
• Main Authors: Shin, Moonsoo, Ryu, Kwangyeol, Jung, Mooyoung
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2012
• Subjects: Agent; Autonomous; Computer simulation; Dynamical systems; Dynamics; Fractal organization; Goal-regulation; Learning; Networks; Neural networks; Production planning; Reinforcement; Reinforcement learning; Self-evolutionary manufacturing system; Fractal organization; Agent; Self-evolutionary manufacturing system; Goal-regulation; Production planning; Reinforcement learning
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2012.01.207

► We propose a goal-regulation mechanism that applies a reinforcement learning approach to autonomous goal-formation. ► Individual goals are regulated by a neural network-based fuzzy inference system called goal-regulation network (GRN). ► The GRN is 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. ► The proposed mechanism is validated by a simulation study on a production planning problem. 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.