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Related Concept Videos

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
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Associative Learning01:27

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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.
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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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For transition metal complexes, the coordination number determines the geometry around the central metal ion. Table 1 compares coordination numbers to molecular geometry. The most common structures of the complexes in coordination compounds are octahedral, tetrahedral, and square planar.
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Related Experiment Video

Updated: Apr 18, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

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Multiagent Learning of Coordination in Loosely Coupled Multiagent Systems.

Chao Yu, Minjie Zhang, Fenghui Ren

    IEEE Transactions on Cybernetics
    |January 17, 2015
    PubMed
    Summary

    This study introduces a multiagent learning (MAL) approach for agents to learn coordinated behaviors in dynamic environments. The method dynamically adapts agent independence, enabling efficient decision-making and near-optimal performance with significant computational savings.

    Related Experiment Videos

    Last Updated: Apr 18, 2026

    The HoneyComb Paradigm for Research on Collective Human Behavior
    06:48

    The HoneyComb Paradigm for Research on Collective Human Behavior

    Published on: January 19, 2019

    10.0K

    Area of Science:

    • Artificial Intelligence
    • Robotics
    • Machine Learning

    Background:

    • Multiagent learning (MAL) enables agents to learn coordinated behaviors in multiagent systems (MASs).
    • Concurrent learning processes in MAL can create non-stationary environments for individual agents.
    • Coordinating agents with unknown domain structures and local observability is challenging.

    Purpose of the Study:

    • To propose a coordinated MAL approach for agents to learn efficient coordinated behaviors.
    • To exploit agent independence in loosely coupled MASs for improved learning.
    • To enable agents to dynamically adapt their independence for efficient decision-making.

    Main Methods:

    • A novel coordinated MAL approach is proposed.
    • Agent independence is explicitly quantified and dynamically adapted during learning.
    • The approach balances single-agent and coordinated learning processes.

    Main Results:

    • The approach was applied to two-robot navigation problems.
    • Agents learned to coordinate or act independently based on environmental context.
    • Significant computational savings and near-optimal performance were achieved.

    Conclusions:

    • The proposed MAL approach effectively enables agents to learn coordinated behaviors.
    • Dynamic adaptation of agent independence is key to efficient decision-making in MASs.
    • This method offers a viable solution for complex navigation tasks in dynamic environments.