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相关概念视频

Decision Making01:20

Decision Making

108
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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Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
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Decision Making: P-value Method01:09

Decision Making: P-value Method

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
5.3K
Uncertainty: Overview00:59

Uncertainty: Overview

552
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
552
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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Observational Learning01:12

Observational Learning

168
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...
168

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相关实验视频

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Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
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在深度不确定性下进行决策的强化学习.

Zhihao Pei1, Angela M Rojas-Arevalo2, Fjalar J de Haan3

  • 1School of Computing and Information Systems, Faculty of Engineering and Information Technology, The University of Melbourne, Australia.

Journal of environmental management
|May 4, 2024
PubMed
概括

强化学习 (RL) 为在不确定性下进行规划提供了自动化的适应性决策. 它补充了多目标进化算法 (MOEA),RL在效率和参数不确定性方面表现出色,而MOEA更好地处理客观不确定性.

关键词:
适应 适应 适应深刻的不确定性 深刻的不确定性探索性建模探索性建模多目标进化算法多目标进化算法强化学习是一种强化学习.坚固性 坚固性

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An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents
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科学领域:

  • 决策科学 决策科学
  • 人工智能的人工智能
  • 计算科学 计算科学

背景情况:

  • 在复杂的不确定性下规划需要适应性策略.
  • 目前用于评估政策绩效的探索方法缺乏固有的适应性,降低决策效率.
  • 自动化适应性政策制定对于有效规划至关重要.

研究的目的:

  • 引入闭环控制的强化学习 (RL) 作为一种用于不确定性规划的新型探索方法.
  • 将RL的性能与多目标进化算法 (MOEA) 的性能进行比较.
  • 评估RL和MOEA在处理不同类型的不确定性方面的效率和稳定性.

主要方法:

  • 在两个假设问题上比较RL和MOEA的计算实验.
  • 利用强化学习 (RL) 与闭环控制用于适应性政策生成.
  • 采用多目标进化算法 (MOEA) 作为探索的基准.

主要成果:

  • 强化学习 (RL) 通过利用勘探历史来证明对参数不确定性的更高效率和政策稳定性.
  • 多目标进化算法 (MOEA) 提供了更直观的客观不确定性量化,从而提高了该领域的稳定性.
  • 在解决规划中的不同方面的不确定性方面,RL和MOEA表现出互补的优势.

结论:

  • 强化学习 (RL) 提供了一种有效的方法,用于在不确定的环境中自动化适应性决策.
  • 在RL和MOEA之间的选择取决于规划问题中普遍存在的特定类型的不确定性 (参数与目标).
  • 研究结果指导研究人员选择最佳的勘探方法,以便在复杂的不确定性下进行规划.