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Related Concept Videos

Role of Shaping in Operant Conditioning01:19

Role of Shaping in Operant Conditioning

Shaping is a technique used in operant conditioning to train complex behaviors by rewarding successive approximations toward the target behavior. This method is necessary because organisms are unlikely to perform complex behaviors spontaneously. Instead, shaping breaks down the desired behavior into small, manageable steps.
The steps involved in shaping begin with reinforcing any response that resembles the desired behavior. For example, parents might praise a child for picking up one toy. As...
Reinforcement01:23

Reinforcement

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.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
Reinforcement Schedules01:24

Reinforcement Schedules

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.
Once a behavior is learned,...
Observational Learning01:12

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...
Spanning Openings in Brick Walls01:20

Spanning Openings in Brick Walls

In brick wall construction, supporting structures are crucial for openings like windows and doors to maintain the integrity and support the weight of the wall above. These supports include lintels, corbels, and arches, each serving specific structural purposes.
Lintels are primary supports used to span openings and can be crafted from materials such as reinforced concrete, steel-reinforced brick masonry, or simple steel angles. These are straightforward to install and are typically concealed...
Purposive Learning01:22

Purposive Learning

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 bonus...

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Related Experiment Video

Updated: May 11, 2026

Automated Visual Cognitive Tasks for Recording Neural Activity Using a Floor Projection Maze
11:15

Automated Visual Cognitive Tasks for Recording Neural Activity Using a Floor Projection Maze

Published on: February 20, 2014

Parsing facades with shape grammars and reinforcement learning.

Olivier Teboul1, Iasonas Kokkinos, Loic Simon

  • 1Ecole Centrale Paris, Grande Voie des Vignes 92290, Chatenay-Malabry, France. olivier.teboul@ecp.fr

IEEE Transactions on Pattern Analysis and Machine Intelligence
|May 18, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces reinforcement learning (RL) for efficient 2D building facade parsing, significantly reducing computation time. The novel approach achieves state-of-the-art results in segmenting architectural elements like windows and doors.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Computational Geometry

Background:

  • Facade parsing is crucial for architectural analysis and urban modeling.
  • Existing methods face computational complexity challenges.
  • Shape grammars offer a structured approach to parsing but can be computationally intensive.

Purpose of the Study:

  • To develop an efficient method for 2D building facade parsing using reinforcement learning (RL).
  • To address the computational complexity inherent in shape grammar-based parsing.
  • To achieve state-of-the-art results in architectural facade segmentation.

Main Methods:

  • Formulating the 1D parsing problem as a Markov Decision Process.
  • Applying RL techniques, including Q-learning and state aggregation, to facade parsing.
  • Developing novel 2D parsing techniques incorporating symmetry and image-based guidance.

Main Results:

  • Achieved state-of-the-art results on the Paris building dataset.
  • Significantly reduced parsing time compared to previous methods.
  • Demonstrated robustness under diverse imaging conditions.

Conclusions:

  • Reinforcement learning effectively tackles the computational complexity of shape grammar-based facade parsing.
  • The proposed method offers an efficient and accurate solution for architectural element segmentation.
  • The approach is validated and made publicly available for further research.