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Related Concept Videos

Purposive Learning01:22

Purposive Learning

381
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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Cognitive Learning01:21

Cognitive Learning

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
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Observational Learning01:12

Observational Learning

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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...
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Introduction to Learning01:18

Introduction to Learning

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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
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Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
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Direct Motor Pathways01:11

Direct Motor Pathways

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The direct motor pathways, also known as the pyramidal tracts, are a group of neural pathways that originate in the brain and descend through the spinal cord. They control the voluntary movement of the body. There are two major direct motor pathways: the corticospinal and the corticobulbar tracts.
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Massive Maritime Path Planning: A Contextual Online Learning Approach.

Pan Zhou, Weiguang Zhao, Jianghui Li

    IEEE Transactions on Cybernetics
    |March 1, 2020
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    Summary
    This summary is machine-generated.

    This study introduces an online learning dispatch approach for optimal ocean vessel path planning. The mission-executing-feedback model enhances big data processing for efficient navigation and improved learning speed.

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    Area of Science:

    • Marine navigation
    • Cybernetics
    • Big data analytics

    Background:

    • Ocean travel path planning is crucial for global trade.
    • Existing methods rely on big data processing but lack comprehensive investigation.
    • Optimizing vessel routes requires advanced computational approaches.

    Purpose of the Study:

    • To propose an online learning dispatch approach for optimal ocean vessel path planning.
    • To enhance big data processing for navigation using the mission-executing-feedback model.
    • To improve the efficiency and accuracy of vessel route selection.

    Main Methods:

    • Developed an online learning dispatch approach based on the mission-executing-feedback (MEF) model.
    • Explored ocean subdomains (OS) to maximize average traveling feedback.
    • Implemented a contextual multiarmed bandit-based algorithm for accurate exploration and fast learning.

    Main Results:

    • The MEF approach achieved a 90% accuracy gain over random exploration.
    • Demonstrated a 25% accuracy improvement compared to other contextual bandit models.
    • Significantly enhanced learning speed in big data online learning scenarios.

    Conclusions:

    • The proposed MEF approach offers a superior method for ocean vessel path planning.
    • Contextual multiarmed bandits significantly improve exploration accuracy and learning efficiency.
    • This research advances big data applications in maritime logistics and navigation.