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Collisions in Multiple Dimensions: Problem Solving01:06

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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
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Associative Learning01:27

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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...
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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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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Cognitive Learning01:21

Cognitive Learning

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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...
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Purposive Learning01:22

Purposive Learning

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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Related Experiment Video

Updated: Aug 23, 2025

The HoneyComb Paradigm for Research on Collective Human Behavior
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Learning multi-agent cooperation.

Corban Rivera1, Edward Staley1, Ashley Llorens2

  • 1Johns Hopkins Applied Physics Lab, Intelligent Systems Center, Laurel, MD, United States.

Frontiers in Neurorobotics
|October 31, 2022
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Summary

This study introduces AI Arena, a scalable framework for artificial intelligence (AI) research, enabling complex multi-agent learning in challenging environments. The framework facilitates cooperation among distributed AI agents, advancing AI applications in urban and defense settings.

Keywords:
artificial intelligencelearned cooperationmulti-agentpolicy learningreinforcement learning

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Multi-Agent Systems

Background:

  • Reinforcement learning (RL) has driven significant AI breakthroughs across various domains.
  • Existing AI frameworks often lack support for complex, distributed, heterogeneous multi-agent systems.
  • This gap limits AI research in critical applications like dense urban environments and defense scenarios.

Purpose of the Study:

  • To introduce the AI Arena, a novel, scalable framework designed for AI research in complex operating environments.
  • To provide flexible abstractions for associating agents, policies, and learning algorithms.
  • To facilitate research on multi-agent learning, cooperation, and curriculum design for advanced AI applications.

Main Methods:

  • Development of the AI Arena: a scalable framework with flexible abstractions.
  • Association of agents with policies and policies with learning algorithms within the framework.
  • Evaluation of the framework's strengths, curriculum design impact, and multi-agent learning paradigms.

Main Results:

  • Demonstrated the strengths of the AI Arena framework in supporting complex AI research.
  • Highlighted the critical role of curriculum design in effective AI agent training.
  • Quantified the impact of multi-agent learning on the emergence of cooperative behaviors.

Conclusions:

  • The AI Arena provides a robust platform for advancing AI research in distributed, heterogeneous multi-agent systems.
  • Effective curriculum design is essential for optimizing learning in complex AI environments.
  • The framework supports the study of cooperation and advanced AI capabilities for real-world applications.