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Reinforcement01:23

Reinforcement

972
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:
972
Reinforcement Schedules01:24

Reinforcement Schedules

541
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
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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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Masking and Demasking Agents01:19

Masking and Demasking Agents

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EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
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Barriers to Effective Communication II01:21

Barriers to Effective Communication II

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The barriers to effective communication also include cultural barriers, semantic barriers, gender barriers, and time constraints.
Cultural barriers:
Differences in values, beliefs, religion, knowledge, and tradition can significantly impact communication. Awareness of nonverbal cues is critical, especially when conversing with a patient from a different culture. What appears appropriate in one culture may be inappropriate in another.
Semantic barriers:
As a result of their tendency to use...
5.1K
Actor-Observer Effect01:23

Actor-Observer Effect

421
The actor-observer effect, a cognitive bias closely linked to the fundamental attribution error, refers to the tendency for individuals to attribute their behavior to external, situational factors while explaining others’ behavior in terms of internal, dispositional traits. This asymmetry in attribution significantly influences social perception and judgment.Cognitive Mechanisms Behind the EffectTwo primary psychological mechanisms contribute to the actor-observer effect: differences in...
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Updated: Feb 20, 2026

Automated Interactive Video Playback for Studies of Animal Communication
07:21

Automated Interactive Video Playback for Studies of Animal Communication

Published on: February 9, 2011

14.1K

マルチエージェント強化学習における堅実で効率的なコミュニケーション

Zejiao Liu1, Yi Li2, Jiali Wang2

  • 1The School of Mathematics, East China University of Science and Technology, Shanghai 200237, China.

Chaos (Woodbury, N.Y.)
|February 18, 2026
PubMed
まとめ
この要約は機械生成です。

この調査は,遅延や限られた帯域幅などの現実世界の制約の下で,マルチエージェント強化学習 (MARL) のための堅牢なコミュニケーションを探求します. 自動運転とフェデレーテッド・ラーニングにおける信頼性の高い MARL システムの戦略を強調しています.

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関連する実験動画

Last Updated: Feb 20, 2026

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科学分野:

  • 人工知能 (AI) とは,人工知能 (AI) のことです.
  • ロボット工学 ロボット工学 ロボット工学
  • 機械学習 (Machine Learning) とは,機械学習 (Machine Learning) について学ぶことです.

背景:

  • マルチエージェント強化学習 (MARL) は,調整されたエージェントの行動を可能にします.
  • 既存のMARL通信モデルは,瞬時の無制限の帯域幅のような非現実的な条件を想定することが多い.

研究 の 目的:

  • 現実的な制約の下で,MARLのための堅牢で効率的なコミュニケーションの進歩を体系的にレビューする.
  • 実践的な応用に焦点を当て,将来の研究方向性を特定すること.

主な方法:

  • MARLのコミュニケーション戦略に関する最近の文献のレビュー.
  • メッセージの混乱,伝送の遅延,および制限された帯域幅を含む課題の分析.
  • 協同型自動運転,分散型SLAM,統合学習におけるアプリケーションに焦点を当てます.

主要な成果:

  • 低遅延の信頼性,帯域幅の使用,およびプライバシーのトレードオフにおける主要な課題の特定.
  • 現実世界の展開に合わせたMARLのコミュニケーション戦略の探索.
  • 理論と実践の間のギャップを埋めるために,現在の研究の統合.

結論:

  • MARLでコミュニケーション,学習,および堅実性を共同設計する必要性があります.
  • 将来の研究は,MARLの実践的な実装のための統一されたアプローチに焦点を当てるべきである.
  • 現実的なコミュニケーションの制約に対処することは,MARLアプリケーションの進歩に不可欠です.