Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Observational Learning

Observational Learning

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...

Associative Learning

Associative Learning

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...

Reinforcement

Reinforcement

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:

Cognitive Learning

Cognitive Learning

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...

Reinforcement Schedules

Reinforcement Schedules

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,...

Introduction to Learning

Introduction to Learning

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Learning Attentional and Gated Communication via Curiosity.

Computational intelligence and neuroscience·2022

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 24, 2025

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Published on: September 27, 2020

Learning Attentional Communication with a Common Network for Multiagent Reinforcement Learning.

Wenwu Yu^1,2, Rui Wang², Xiaohui Hu²

¹University of Chinese Academy of Sciences, Beijing 100049, China.

Computational Intelligence and Neuroscience

|July 7, 2023

Summary

This summary is machine-generated.

This study introduces a new multiagent communication algorithm (MAACCN) that enhances cooperation in partially observable environments by incorporating historical agent network data. MAACCN significantly boosts performance, especially in challenging scenarios.

More Related Videos

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Automated Interactive Video Playback for Studies of Animal Communication

Automated Interactive Video Playback for Studies of Animal Communication

Published on: February 9, 2011

Related Experiment Videos

Last Updated: Jul 24, 2025

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Published on: September 27, 2020

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Automated Interactive Video Playback for Studies of Animal Communication

Automated Interactive Video Playback for Studies of Animal Communication

Published on: February 9, 2011

Area of Science:

Artificial Intelligence
Multiagent Systems
Reinforcement Learning

Background:

Existing multiagent systems often limit information to current network states, hindering cooperation in partially observable environments.
Effective communication and cooperation are crucial for complex tasks but are constrained by limited information sources.

Purpose of the Study:

To propose a novel algorithm, multiagent attentional communication with the common network (MAACCN), to expand information sources for multiagent communication.
To improve decision-making by integrating current observations with historical consensus knowledge.

Main Methods:

Developed MAACCN, incorporating a consensus information module that leverages historical best-performing networks as a common network.
Utilized an attention mechanism to combine current observations with extracted consensus knowledge for enhanced information inference.
Evaluated MAACCN on the StarCraft multiagent challenge (SMAC) benchmark.

Main Results:

MAACCN demonstrated superior performance compared to existing baseline algorithms.
The proposed algorithm achieved over a 20% performance improvement in a particularly difficult scenario.
Experimental results validate the effectiveness of MAACCN in enhancing multiagent cooperation.

Conclusions:

MAACCN effectively expands information sources by incorporating historical consensus knowledge, leading to improved multiagent communication and cooperation.
The algorithm shows significant potential for advancing research in partially observable environments and complex multiagent tasks.
Future work can explore further enhancements to the consensus mechanism and its application in diverse domains.