Learning answer set programs with aggregates via sampling and genetic programming

The goal of inductive logic programming is to learn a logic program that models the examples provided as input. The search space of the possible programs is constrained by a language bias, which defines the atoms and literals allowed in rules. Answer set programming is a powerful formalism to repres...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Machine learning Ročník 114; číslo 7; s. 148
Hlavný autor: Azzolini, Damiano
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.07.2025
Springer Nature B.V
Predmet:
Aggregates
Artificial Intelligence
Bias
Combinatorial analysis
Computer Science
Control
Declarative programming
Genetic algorithms
Language
Logic programming
Logic programs
Machine Learning
Mathematical programming
Mechatronics
Natural Language Processing (NLP)
Robotics
Simulation and Modeling
Variables
Inductive logic programming
Answer set programming
Genetic programming
ISSN:0885-6125, 1573-0565
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:The goal of inductive logic programming is to learn a logic program that models the examples provided as input. The search space of the possible programs is constrained by a language bias, which defines the atoms and literals allowed in rules. Answer set programming is a powerful formalism to represent complex combinatorial domains, also thanks to syntactic constructs such as aggregates. However, learning answer set programs from data is challenging, and often existing tools do not support the specification of aggregates in the language bias. In this paper, we introduce GENTIANS, a tool based on a genetic algorithm to learn answer set programs possibly with aggregates, arithmetic, and comparison operators, from examples. Empirical results, also against an existing solver, show that GENTIANS is able to provide accurate solutions even when the search space contains millions of clauses. Additionally, experiments on noisy datasets show the effectiveness of our approach.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-025-06780-7