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

Purposive Learning01:22

Purposive Learning

110
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...
110
Cognitive Learning01:21

Cognitive Learning

237
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...
237
The Availability Heuristic01:08

The Availability Heuristic

5.9K
A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
5.9K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

48
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
48

You might also read

Related Articles

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

Sort by
Same author

2D and 3D Angles-Only Target Tracking Based on Maximum Correntropy Kalman Filters.

Sensors (Basel, Switzerland)·2022
Same author

Intermittent Information-Driven Multi-Agent Area-Restricted Search.

Entropy (Basel, Switzerland)·2020
Same author

Autonomous Exploration and Mapping with RFS Occupancy-Grid SLAM.

Entropy (Basel, Switzerland)·2020
Same author

GLMB Tracker with Partial Smoothing.

Sensors (Basel, Switzerland)·2019
Same author

Data Association for Multi-Object Tracking via Deep Neural Networks.

Sensors (Basel, Switzerland)·2019

Related Experiment Video

Updated: Jun 23, 2025

An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles
09:27

An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles

Published on: August 25, 2020

4.2K

A Possibilistic Formulation of Autonomous Search for Targets.

Zhijin Chen1, Branko Ristic1, Du Yong Kim1

  • 1School of Engineering, RMIT University, 376-392 Swanston Street, Melbourne, VIC 3000, Australia.

Entropy (Basel, Switzerland)
|June 26, 2024
PubMed
Summary

This study introduces a novel approach to autonomous search using possibility theory, enhancing target localization under uncertainty. The new method offers improved quantitative modeling for partially known detection probabilities.

Keywords:
autonomous systemspossibility theoryrobust estimation

More Related Videos

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches
07:23

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Published on: August 4, 2014

23.0K
A Dual Task Procedure Combined with Rapid Serial Visual Presentation to Test Attentional Blink for Nontargets
08:45

A Dual Task Procedure Combined with Rapid Serial Visual Presentation to Test Attentional Blink for Nontargets

Published on: December 5, 2014

9.2K

Related Experiment Videos

Last Updated: Jun 23, 2025

An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles
09:27

An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles

Published on: August 25, 2020

4.2K
Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches
07:23

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Published on: August 4, 2014

23.0K
A Dual Task Procedure Combined with Rapid Serial Visual Presentation to Test Attentional Blink for Nontargets
08:45

A Dual Task Procedure Combined with Rapid Serial Visual Presentation to Test Attentional Blink for Nontargets

Published on: December 5, 2014

9.2K

Area of Science:

  • Robotics and Artificial Intelligence
  • Information Theory and Signal Processing

Background:

  • Autonomous search relies on sensing, estimation, and motion control for target localization.
  • Traditional methods use Bayesian estimation and information theory, which can struggle with epistemic uncertainty.

Purpose of the Study:

  • To formulate autonomous search within the framework of possibility theory.
  • To address quantitative modeling and reasoning challenges posed by epistemic uncertainty in search operations.

Main Methods:

  • Developed a possibilistic formulation for autonomous search.
  • Introduced a Bayes-like solution for sequential estimation.
  • Defined a motion control reward function accounting for epistemic uncertainty.

Main Results:

  • Demonstrated quantitative modeling for partially known probabilities of detection (interval values).
  • Showcased an elegant Bayes-like estimation approach.
  • Validated the enhanced search algorithm's advantages through numerical simulations.

Conclusions:

  • Possibility theory offers a robust framework for autonomous search with epistemic uncertainty.
  • The proposed algorithm provides a more sophisticated approach to target localization.
  • The method is effective in scenarios with incomplete detection information.