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相关概念视频

Reinforcement01:23

Reinforcement

177
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:
177
Introduction to Learning01:18

Introduction to Learning

326
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
326
Elaborative Rehearsals01:07

Elaborative Rehearsals

77
Elaborative rehearsal is a crucial cognitive strategy that strengthens information encoding in long-term memory by making meaningful connections between new data and pre-existing knowledge. This approach contrasts with maintenance rehearsal, which involves simple repetition without delving into the significance of the information. While maintenance rehearsal might temporarily keep information active in short-term memory, it is less effective for long-term retention.
The effectiveness of...
77
Observational Learning01:12

Observational Learning

123
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...
123
Cognitive Learning01:21

Cognitive Learning

219
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
219
Associative Learning01:27

Associative Learning

283
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
283

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Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
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深度渐变强化学习用于云计算框架中的音乐即兴演奏.

Fadwa Alrowais1, Munya A Arasi2, Saud S Alotaibi3

  • 1Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

PeerJ. Computer science
|February 3, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了使用强化学习 (RL) 进行实时音乐即兴演奏的人工智能 (AI). 人工智能模型产生了和连贯和美学上有趣的音乐作品,优于现有的方法.

关键词:
云框架 云框架 云框架集装箱化 集装箱化有门的经常性单位.音乐即兴表演 音乐即兴表演强化学习是一种强化学习.

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科学领域:

  • 音乐 技术 音乐 技术
  • 人工智能的人工智能
  • 计算创造力的创造力

背景情况:

  • 实时音乐即兴演奏在创作动态和灵活的作曲方面提出了挑战.
  • 人工智能 (AI) 为增强人类在音乐中的创造力提供了潜在的解决方案.
  • 强化学习 (RL) 被探索为开发交互式音乐创作系统的方法.

研究的目的:

  • 探索使用强化学习 (RL) 技术来创建交互式和响应式的音乐即兴系统.
  • 开发一种能够导航音乐可能性的AI代理,用于实时即兴演奏.
  • 为了产生美学上有趣和和地凝聚在一起的音乐即兴表演.

主要方法:

  • 利用双向封闭的反复单元来识别音乐数据中的旋律框架.
  • 将音乐元素 (音符,和弦,节奏) 转换为适合RL输入的格式.
  • 采用基于深度梯度的强化学习技术,并采用定制奖励系统.
  • 在容器化云环境中对Bach Chorales数据集训练RL代理.
  • 在MIDI格式中染即兴音乐.

主要成果:

  • 拟议的AI模型实现了特定的性能指标:Pitch Frequency (PF) +0.15,标准Pitch Delay (SPD) -0.43,峰值之间的平均距离 (ADP) -0.07,音符持续时间梯度 (NDG) -0.0041.
  • 这些结果表明,与其他音乐即兴演奏方法相比,其表现优越.
  • 该模型展示了产生和连贯和美学上有趣的即兴表演的能力.

结论:

  • 强化学习为开发复杂的人工智能驱动的音乐即兴系统提供了一种可行的方法.
  • 深度学习技术与RL的整合使得创作新且高品质的音乐作品成为可能.
  • 拟议的方法显示了在音乐中推进计算创造力领域的前景.