Jove
Visualize
Contact Us

Related Concept Videos

Sampling Plans01:23

Sampling Plans

316
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
316

You might also read

Related Articles

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

Sort by
Same author

Explainable AI for suicide risk detection: gender- and age-specific patterns from real-time crisis chats.

Frontiers in medicine·2026
Same author

Predicting imminent suicide risk in a crisis hotline chat using machine learning.

Scientific reports·2025
Same author

Real-time emotional distress during a national trauma: Changes in suicidality, depression, and loneliness among helpline users in the aftermath of October 7, 2023 terror attack in Israel.

Psychiatry research·2025
Same author

Joint coordination constraints using an upper limb exoskeleton impact novel skill acquisition.

Wearable technologies·2025
Same author

Education Challenges Among Early Career Researchers in Medical Informatics.

Studies in health technology and informatics·2024
Same author

Self-Reported Learning Strategies and Preferences in Health Informatics.

Studies in health technology and informatics·2024
Same journal

Structural impact of non-IID heterogeneity on federated behavioral anomaly detection in IoT and IoMT systems.

Frontiers in artificial intelligence·2026
Same journal

DiscoVerse: multi-agent pharmaceutical co-scientist for traceable drug discovery and reverse translation.

Frontiers in artificial intelligence·2026
Same journal

EEG-based cognition-aware task classification and scheduling using enhanced fuzzy transition modeling.

Frontiers in artificial intelligence·2026
Same journal

Autofluorescence and deep learning in early disease detection: biological foundations, clinical applications, and future directions.

Frontiers in artificial intelligence·2026
Same journal

Legal document summarization: a short review.

Frontiers in artificial intelligence·2026
Same journal

Generative AI adoption and its impact on teachers' self-efficacy and instructional confidence in Ghana.

Frontiers in artificial intelligence·2026
See all related articles
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 Experiment Video

Updated: Oct 5, 2025

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant
05:18

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Published on: October 6, 2023

1.5K

Comparing Plan Recognition Algorithms Through Standard Plan Libraries.

Reuth Mirsky1, Ran Galun2, Kobi Gal2,3

  • 1Department of Computer Science, The University of Texas at Austin, Austin, TX, United States.

Frontiers in Artificial Intelligence
|January 24, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a standardized plan library for AI plan recognition, enabling fair comparison of algorithms. A new algorithm developed from these insights outperforms existing methods in expressiveness and efficiency.

Keywords:
artificial intelligenceplan librariesplan recognitionstandardizationtheory of mind

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

672
Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System
08:25

Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System

Published on: April 11, 2018

15.5K

Related Experiment Videos

Last Updated: Oct 5, 2025

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant
05:18

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Published on: October 6, 2023

1.5K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

672
Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System
08:25

Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System

Published on: April 11, 2018

15.5K

Area of Science:

  • Artificial Intelligence
  • Cognitive Science

Background:

  • Plan recognition, a core AI problem, lacks standardized representations and comparison methods.
  • Existing approaches are applied across diverse fields like user interfaces and cybersecurity.

Purpose of the Study:

  • To establish a standard plan library representation for hierarchical, discrete-space plan recognition.
  • To provide evaluation criteria for comparing plan recognition algorithms.
  • To thoroughly compare two known algorithms, SBR and PHATT, using the new standard.

Main Methods:

  • Developed a comprehensive, standard plan library representation.
  • Theoretically and empirically compared SBR and PHATT algorithms.
  • Analyzed the impact of plan library properties on recognition complexity.

Main Results:

  • Demonstrated a trade-off between expressiveness and efficiency; SBR is faster but less functional than PHATT.
  • Identified plan library characteristics influencing recognition complexity irrespective of the algorithm.
  • Developed a novel algorithm that surpasses existing methods in both expressiveness and efficiency.

Conclusions:

  • The standardized representation facilitates algorithm comparison and development.
  • Algorithm performance is influenced by plan library design.
  • The new algorithm offers improved expressiveness and efficiency for plan recognition tasks.