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相关概念视频

The Availability Heuristic01:08

The Availability Heuristic

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A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
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Reason and Intuition01:37

Reason and Intuition

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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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Heuristics01:21

Heuristics

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Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
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The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

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In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
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Decision Making01:20

Decision Making

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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
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Perception01:28

Perception

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Perception is a fundamental psychological process that enables individuals to organize, interpret, and consciously experience sensory information. This process is crucial for understanding and interacting with the world around us. It includes both bottom-up and top-down processing, each playing a distinct role in how we perceive our environment.
Bottom-up processing begins at the sensory level, where receptors detect external environmental stimuli. These could include the tactile sensation of...
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Updated: Jun 6, 2025

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients
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启发式满足人类和机器人的积极感知中的推断决策.

Yucheng Chen1, Pingping Zhu2, Anthony Alers3

  • 1Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY, United States.

Frontiers in robotics and AI
|November 27, 2024
PubMed
概括

机器人现在可以通过积极感知来调整他们的搜索策略,在最佳和启发式方法之间切换,在复杂的环境中成功,尽管有意想不到的挑战. 这提高了机器人在现实世界中完成任务的情况.

关键词:
积极的感知感知活动感知.在决策过程中做出决定.启发式听觉学是一种启发式听觉学.人类 人类 人类 人类 人类 人类 人类这是一个令人满意的满足.传感器 传感器 传感器研究研究研究研究研究研究.在寻找宝藏的过程中,

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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
  • 认知科学 认知科学

背景情况:

  • 决策算法在现实世界机器人中经常因为违反假设和外部压力而失败.
  • 人类通过平衡最佳和启发式策略,利用环境线索,有效地应对不确定性.

研究的目的:

  • 为机器人在压力下工作开发积极感知解决方案的总体方法.
  • 研究和建模机器人如何在最佳和启发式决策之间进行调节.

主要方法:

  • 在虚拟世界中模拟"寻宝"问题,以学习可概括的策略.
  • 为配备相机的机器人开发了主动感知算法.
  • 在高准确度的数值模拟和物理实验中测试算法.

主要成果:

  • 新的积极感知算法在"寻宝"基准中表现优于现有方法 (细胞分解,信息路线图,信息潜力).
  • 算法在时间限制,资源限制和恶劣天气 (雾) 等意想不到的条件下表现出有效性.

结论:

  • 开发的积极感知策略使机器人能够调整他们的决策,在充满挑战的动态环境中成功完成任务.
  • 这种方法为现实世界机器人应用提供了强大的解决方案,其中环境模型不完整或压力很高.