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Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
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Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Strategy for maximizing space utilization in smart libraries based on reinforcement learning.

Ying Dai1

  • 1Zhejiang Gongshang University Hangzhou College of Commerce, Hangzhou, 311508, Zhejiang, China. daiying188@outlook.com.

Scientific Reports
|November 12, 2025
PubMed
Summary
This summary is machine-generated.

Modern libraries can maximize space with Reinforcement Learning Maximize Space Utilization (RLMSU). This AI approach optimizes seating and navigation, improving user satisfaction and operational efficiency in dynamic library environments.

Keywords:
Action-agentAnd user satisfactionOperational efficiencyReinforcement learningSeat availabilitySmart librariesSpace utilization

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Area of Science:

  • Library Science
  • Artificial Intelligence
  • Computer Science

Background:

  • Traditional library designs struggle with dynamic user demands, leading to underutilization and overcrowding.
  • Current systems fail to adapt to real-time user behavior, impacting space efficiency.
  • Inefficient spatial arrangements hinder navigation and resource accessibility.

Purpose of the Study:

  • To introduce Reinforcement Learning Maximize Space Utilization (RLMSU) methodologies for dynamic library space management.
  • To address research problems related to space underutilization and overcrowding in modern libraries.
  • To optimize library resource allocation and improve user experience.

Main Methods:

  • Developed the RLMSU platform using IoT sensors, historical data, and computer vision.
  • Employed the agent-action (AA) paradigm within Reinforcement Learning to predict user occupancy and movement.
  • Utilized Python for data analysis and framework development with the Full Library Services Dataset.

Main Results:

  • RLMSU implementation increased seating availability by 30%.
  • Congestion was reduced by 25% through optimized space allocation.
  • The system demonstrated effectiveness in dynamic scenarios, enhancing user satisfaction during peak and off-peak hours.

Conclusions:

  • RLMSU methodologies successfully optimize dynamic space utilization in libraries.
  • The system improves operational efficiency and user satisfaction in diverse library settings.
  • Integration with research, university, and public libraries confirmed its practical applicability.