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相关概念视频

Reinforcement01:23

Reinforcement

354
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:
354
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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Reinforcement Schedules01:24

Reinforcement Schedules

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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.
Once a behavior is learned,...
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Evolutionary Psychology01:20

Evolutionary Psychology

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Evolutionary psychology explores the origins of human behavior and mental processes by framing them within the context of natural selection, a theory famously propounded by Charles Darwin. This field asserts that many behaviors common across human societies — ranging from instinctive fear reactions to complex social interactions — arose as evolutionary adaptations. These adaptations enhanced the survival and reproductive success of our ancestors, thereby becoming embedded in the...
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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

150
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
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Convergent Evolution01:54

Convergent Evolution

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Evolution shapes the features of organisms over time, ensuring that they are suited for the environments in which they live. Sometimes, selection pressure leads to the rise of similar but unrelated adaptations in organisms with no recent common ancestors, a process known as convergent evolution.
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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复杂网络的动态演变:一种强化学习方法,将进化游戏应用于社区结构.

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    此摘要是机器生成的。

    本研究介绍了一个网络进化模型,其中包括出生-死亡过程和强化学习,以了解复杂系统中的社区形成. 该模型准确地预测了现实世界的人口动态和社区结构.

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    科学领域:

    • 复杂系统科学 复杂系统科学
    • 网络科学 网络科学
    • 计算社会科学 计算社会科学

    背景情况:

    • 目前关于动态系统的研究缺乏个人出生-死亡和社区发展的模型.
    • 了解复杂系统中的新兴结构需要将个人行为与网络动态相结合.

    研究的目的:

    • 提出一种新的联网进化模型,结合出生-死亡过程和强化学习.
    • 研究合作行为和社区结构的出现和演变.
    • 用现实数据验证模型的实用性.

    主要方法:

    • 开发了一个网络进化模型,包括个体的出生-死亡,Q学习强化学习和空间运动.
    • 模拟系统与或没有出生死亡过程,以观察行为和结构的进化.
    • 经过验证的模型与真实世界的人口和网络数据相匹配.

    主要成果:

    • 该模型成功地重现了合作行为和社区结构.
    • 剥削率和回报参数被确定为社区出现的关键驱动因素.
    • 学习率,折扣因素和空间尺寸影响社区形成的速度,稳定性和规模.

    结论:

    • 拟议的模型为动态系统中的社区发展提供了新的视角.
    • 它为研究人口动态和新兴网络结构提供了一个强大的框架.
    • 该模型的参数为管理社区形成和稳定的因素提供了洞察力.