Paper Info
Title
An ILP System for Learning Head Output Connected Predicates
Abstract
Inductive Logic Programming (ILP) [1] systems are general purpose learners that have had significant success on solving a number of relational problems, particularly from the biological domain [2,3,4,5]. However, the standard compression guided top-down search algorithm implemented in state of the art ILP systems like Progol [6] and Aleph [7] is not ideal for the Head Output Connected (HOC) class of learning problems. HOC is the broad class of predicates that have at least one output variable in the target concept. There are many relevant learning problems of this class such as arbitrary arithmetic functions and list manipulation predicates which are useful in areas such as automated software verification[8]. In this paper we present a special purpose ILP system to efficiently learn HOC predicates.
Year
DOI
Venue
2009
10.1007/978-3-642-04686-5_13
EPIA '89
Keywords
Field
DocType
connected predicates,art ilp system,general purpose learner,broad class,arbitrary arithmetic function,learning head output,automated software verification,special purpose ilp system,head output,ilp system,inductive logic programming,hoc predicate,relevant learning problem,top down,search algorithm,software verification,arithmetic function
Inductive logic programming,PROGOL,Arithmetic function,Search algorithm,General purpose,Computer science,Theoretical computer science,Artificial intelligence,Predicate (grammar),Machine learning,Software verification
Conference
Volume
ISSN
Citations 
5816
0302-9743
5
PageRank 
References 
Authors
0.44
11
3
Name
Order
Citations
PageRank
José C. Santos150.44
Alireza Tamaddoni-Nezhad226918.66
Stephen Muggleton33915619.54