Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Observational Learning01:12

Observational Learning

372
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...
372
Cognitive Learning01:21

Cognitive Learning

696
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...
696
Purposive Learning01:22

Purposive Learning

219
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...
219
Associative Learning01:27

Associative Learning

654
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...
654
Introduction to Learning01:18

Introduction to Learning

596
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...
596
Force Classification01:22

Force Classification

1.8K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.8K

You might also read

Related Articles

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

Sort by
Same author

MATRIX: Mental heAlth diagnostics Through Real time Intelligent unified X-AI attribution reasoning.

Frontiers in digital health·2026
Same author

Constructing a metadata knowledge graph as an atlas for demystifying AI pipeline optimization.

Frontiers in big data·2025
Same author

Evaluating the Role of Data Enrichment Approaches towards Rare Event Analysis in Manufacturing.

Sensors (Basel, Switzerland)·2024
Same author

RI2AP: Robust and Interpretable 2D Anomaly Prediction in Assembly Pipelines.

Sensors (Basel, Switzerland)·2024
Same author

Detecting Substance Use Disorder Using Social Media Data and the Dark Web: Time- and Knowledge-Aware Study.

JMIRx med·2024
Same author

Ki-Cook: clustering multimodal cooking representations through knowledge-infused learning.

Frontiers in big data·2023
Same journal

Inner layer security reinforcement for instant payment systems: a dual layer encryption-steganography evaluation in Brunei's digital payment context.

Frontiers in big data·2026
Same journal

Measuring the impact of virtualization and containerization on the environment when using GPUs for processing the AI models.

Frontiers in big data·2026
Same journal

Using artificial intelligence to improve governance and public services in Africa.

Frontiers in big data·2026
Same journal

Case count metric for comparative analysis of entity resolution results.

Frontiers in big data·2026
Same journal

Data field theory: a geometric framework for learning on Riemannian manifolds with synthetic validation and limitation analysis.

Frontiers in big data·2026
Same journal

Correction: Explainable gradient convolutional vector fuzzy pattern analysis based on ensemble model for facial expression recognition.

Frontiers in big data·2026
See all related articles

Related Experiment Video

Updated: Oct 10, 2025

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.6K

Knowledge-infused Learning for Entity Prediction in Driving Scenes.

Ruwan Wickramarachchi1, Cory Henson2, Amit Sheth1

  • 1Artificial Intelligence Institute, University of South Carolina, Columbia, SC, United States.

Frontiers in Big Data
|December 13, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces Knowledge-based Entity Prediction (KEP) for autonomous driving, improving scene understanding by predicting missing entities using knowledge graphs. The neuro-symbolic approach significantly outperforms baseline methods in complex urban driving scenarios.

Keywords:
autonomous drivingentity predictionknowledge graph embeddingsknowledge-infused learningneuro-symbolic computingscene understanding

More Related Videos

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.2K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

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

Published on: December 15, 2023

4.4K

Related Experiment Videos

Last Updated: Oct 10, 2025

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.6K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.2K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

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

Published on: December 15, 2023

4.4K

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Autonomous Systems

Background:

  • Scene understanding is crucial for autonomous driving, requiring accurate and complete semantic interpretation of environments.
  • Knowledge graphs (KGs) can represent scene entities and relations, aiding in improved scene understanding through entity prediction.

Purpose of the Study:

  • To define and formalize the problem of Knowledge-based Entity Prediction (KEP) for enhancing autonomous driving scene understanding.
  • To develop and present an innovative neuro-symbolic solution for KEP that leverages semantic knowledge.

Main Methods:

  • Introduced a dataset-agnostic ontology for describing driving scenes.
  • Utilized expressive, holistic scene representations via knowledge graphs.
  • Mapped KEP to link prediction (LP) using knowledge-graph embeddings (KGE) within a neuro-symbolic framework.

Main Results:

  • Demonstrated the effectiveness of the neuro-symbolic KEP approach on complex urban driving data.
  • Achieved high precision (0.87 Hits@1) in predicting missing entities.
  • Significantly outperformed non-semantic and rule-based baseline methods.

Conclusions:

  • The proposed neuro-symbolic KEP method effectively enhances scene understanding in autonomous driving.
  • Leveraging semantic knowledge through KGs and KGEs offers a powerful approach for predicting unrecognized entities.
  • This method shows significant potential for improving the safety and reliability of autonomous vehicles.