Generalisation through Negation and Predicate Invention
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F24%3A00581803" target="_blank" >RIV/67985807:_____/24:00581803 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1609/aaai.v38i9.28915" target="_blank" >https://doi.org/10.1609/aaai.v38i9.28915</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1609/aaai.v38i9.28915" target="_blank" >10.1609/aaai.v38i9.28915</a>
Alternative languages
Result language
angličtina
Original language name
Generalisation through Negation and Predicate Invention
Original language description
The ability to generalise from a small number of examples is a fundamental challenge in machine learning. To tackle this challenge, we introduce an inductive logic programming (ILP) approach that combines negation and predicate invention. Combining these two features allows an ILP system to generalise better by learning rules with universally quantified body-only variables. We implement our idea in NOPI, which can learn normal logic programs with predicate invention, including Datalog programs with stratified negation. Our experimental results on multiple domains show that our approach can improve predictive accuracies and learning times.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GF22-06414L" target="_blank" >GF22-06414L: Proof analysis AND Automated deduction FOr REcursive STructures</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 38th AAAI Conference on Artificial Intelligence
ISBN
978-1-57735-887-9
ISSN
2159-5399
e-ISSN
—
Number of pages
9
Pages from-to
10467-10475
Publisher name
AAAI Press
Place of publication
Washington, DC
Event location
Vancouver
Event date
Feb 20, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001241512400118