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相关概念视频

Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
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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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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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Two-Dimensional Force System: Problem Solving01:29

Two-Dimensional Force System: Problem Solving

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Solving problems related to two-dimensional force systems is an essential aspect of mechanics and engineering. By applying the principles of vector analysis and force equilibrium, one can determine the effect of multiple forces acting on an object in a two-dimensional space.
The first step to solving a two-dimensional force system problem is to draw a free-body diagram of the object under consideration. This diagram helps identify all the external forces acting on the object, including their...
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Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

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A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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相关实验视频

Updated: Jul 17, 2025

Author Spotlight: Investigating the Effects of Mind-Body-Movement Practices on Brain Function
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Author Spotlight: Investigating the Effects of Mind-Body-Movement Practices on Brain Function

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什么时候切换:为部分可观测的多代理路径寻找计划和学习

Alexey Skrynnik, Anton Andreychuk, Konstantin Yakovlev

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    |August 31, 2023
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    概括
    此摘要是机器生成的。

    这项研究引入了部分可观测的多代理途径 (PO-MAPF) 的新政策. 结合启发式搜索和强化学习 (RL) 的混合策略显示出卓越的性能和概括能力.

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    相关实验视频

    Last Updated: Jul 17, 2025

    Author Spotlight: Investigating the Effects of Mind-Body-Movement Practices on Brain Function
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    Published on: January 26, 2024

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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
    • 计算机科学 计算机科学

    背景情况:

    • 多代理路径查找 (MAPF) 对于在共享环境中协调多个代理来说至关重要.
    • 传统的MAPF假定完全可观测,这往往是不现实的.
    • 部分可观测的MAPF (PO-MAPF) 由于有限的代理人感知和缺乏沟通而带来了挑战.

    研究的目的:

    • 开发和评估针对PO-MAPF问题的新政策.
    • 研究一种混合政策方法,将启发式搜索和强化学习 (RL) 结合起来.
    • 根据最先进的方法评估拟议政策的性能和概括能力.

    主要方法:

    • 提出了两个新的政策:一个基于启发式搜索,另一个基于强化学习 (RL).
    • 引入了混合政策,在启发式方法和RL方法之间动态切换.
    • 实施了三个切换策略:启发式,决定式和可学习式.

    主要成果:

    • 混合政策的表现优于单个启发式和RL政策.
    • 混合政策有效地对未见的地图和问题实例进行了概括.
    • 性能优于现有的最先进的PO-MAPF算法,如PRIMAL2和PICO.

    结论:

    • 混合政策方法在解决PO-MAPF问题方面非常有效.
    • 建议的切换策略提高了在动态环境中的适应性和性能.
    • 开发的方法在部分可观测性下在自主代理协调方面取得了重大进展.