TY - GEN
T1 - VSM-ACT-R
T2 - 17th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2024
AU - Wu, Siyu
AU - Oltramari, Alessandro
AU - Ritter, Frank E.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - The era of Industry 4.0 demands innovative solutions to produce high-quality products within tight lead times. This paper explores the integration of cognitive architectures (CAs) into manufacturing solutions, with a focus on using VSM-ACT-R, a cognitive architecture model built upon the ACT-R architecture. VSM-ACT-R aids in making informed decisions in smart scheduling that boosts productivity while ensuring consistent quality. The model stands out in three key aspects of decision-making in manufacturing: First, it executes tasks using decision-making algorithms and knowledge representations observed in human subjects, supported by declarative memories that reflect intuitive and domain-specific knowledge. Second, it mimics various levels of decision-making-from novice through to expert—using production rules and retrieval mechanisms that replicate variations of human behavior. Third, it simulates the learning processes of decision-makers, managed by a decision-choice control center that is driven by utility learning and reinforcement reward. We conclude by discussing an evaluation of this model, its applications, and its implications.
AB - The era of Industry 4.0 demands innovative solutions to produce high-quality products within tight lead times. This paper explores the integration of cognitive architectures (CAs) into manufacturing solutions, with a focus on using VSM-ACT-R, a cognitive architecture model built upon the ACT-R architecture. VSM-ACT-R aids in making informed decisions in smart scheduling that boosts productivity while ensuring consistent quality. The model stands out in three key aspects of decision-making in manufacturing: First, it executes tasks using decision-making algorithms and knowledge representations observed in human subjects, supported by declarative memories that reflect intuitive and domain-specific knowledge. Second, it mimics various levels of decision-making-from novice through to expert—using production rules and retrieval mechanisms that replicate variations of human behavior. Third, it simulates the learning processes of decision-makers, managed by a decision-choice control center that is driven by utility learning and reinforcement reward. We conclude by discussing an evaluation of this model, its applications, and its implications.
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U2 - 10.1007/978-3-031-72241-7_7
DO - 10.1007/978-3-031-72241-7_7
M3 - Conference contribution
AN - SCOPUS:85205090098
SN - 9783031722400
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 69
EP - 79
BT - Social, Cultural, and Behavioral Modeling - 17th International Conference, SBP-BRiMS 2024, Proceedings
A2 - Thomson, Robert
A2 - Pyke, Aryn
A2 - Hariharan, Aravind
A2 - Renshaw, Scott
A2 - Park, Patrick
A2 - Al-khateeb, Samer
A2 - Burger, Annetta
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 18 September 2024 through 20 September 2024
ER -