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相关概念视频

Observational Learning01:12

Observational Learning

225
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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Purposive Learning01:22

Purposive Learning

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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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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Modeling and Similitude01:12

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Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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从单个专家数据集学习规定的安全性能模拟学习.

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    此摘要是机器生成的。

    拉格朗的生成对抗模仿学习 (LGAIL) 能够在各种约束下进行自适应的安全政策学习. 这种方法动态地平衡模仿和安全,优于现有的安全模仿学习方法.

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    科学领域:

    • 机器人技术 机器人技术 机器人技术
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 现有的安全模仿学习 (safe IL) 方法主要集中在复制专家政策上,这些政策可能无法满足不同的或新的安全约束.
    • 这种限制阻碍了在需要适应性安全规范的场景中应用安全IL.

    研究的目的:

    • 提出一种新的算法,拉格朗的生成对抗模拟学习 (LGAIL),能够在各种安全约束下从专家数据中自适应学习安全策略.
    • 解决当前安全IL方法在处理各种安全要求方面的局限性.

    主要方法:

    • 增加具有安全约束的生成对抗模拟学习 (GAIL) 并使用拉格朗奇乘数将其放松为不受约束的问题.
    • 采用两阶段的优化:用于数据相似性的区分器训练和用于模仿和安全的前向增强学习,由动态调整的拉格朗日乘数指导.

    主要成果:

    • 在规定的约束条件下,理论分析证明了LGAIL的融合和安全保证.
    • 在OpenAI安全健身房进行了广泛的实验,验证了拟议方法的有效性.

    结论:

    • LGAIL有效地学习适应性安全政策,满足各种安全约束,优于现有的安全IL方法.
    • 拉格朗奇乘数的动态调整对于平衡模仿性能和安全遵守至关重要.