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 Experiment Videos

Learning to solve planning problems efficiently by means of genetic programming.

R Aler1, D Borrajo, P Isasi

  • 1Department of Computer Science, Universidad Carlos III de Madrid, 28911 Leganés, Madrid, Spain. aler@inf.uc3m.es

Evolutionary Computation
|November 16, 2001
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

A selective learning method to improve the generalization of multilayer feedforward neural networks.

International journal of neural systems·2003
See all related articles

This study evolves heuristics for planning systems, improving efficiency by adapting existing planners. The EvoCK approach enhances problem-solving across various planning domains.

Area of Science:

  • Artificial Intelligence
  • Computer Science

Background:

  • Declarative problem solving, like planning, presents challenges for Genetic Programming (GP).
  • Existing GP applications in planning involve searching plan space or evolving entire planners.

Purpose of the Study:

  • To evolve heuristics to enhance the efficiency of existing planning systems.
  • To offer a more feasible and efficient alternative to evolving entire planners or searching plan space.

Main Methods:

  • Evolving heuristics using Genetic Programming (GP) for specific planning domains.
  • Introducing and utilizing an Instance-Based Crossover genetic operator.
  • Leveraging traces from a base planner as genetic material.

Main Results:

Related Experiment Videos

  • The EvoCK approach successfully evolved heuristics that improved the performance of the PRODIGY4.0 planner.
  • Demonstrated effectiveness in the blocks world and logistics planning domains.
  • Instance-Based Crossover proved capable of utilizing planner traces for evolution.

    • Conclusions:

    • Evolving heuristics is a viable strategy for improving planner efficiency.
    • The EvoCK approach offers a more efficient and adaptable method for AI planning.
    • The Instance-Based Crossover operator shows promise for GP-based planning.