Paper Info
Title
Techniques to enhance efficiency and effectiveness of inductive logic programming systems: the TWEETY approaches.
Abstract
This paper presents four novel approaches to enhance efficiency and effectiveness of Inductive Logic Programming (ILP) systems, along with their implementation in a new ILP system, called TWEETY. The proposed approaches include (1) a new declaration mechanism, called connection declarations, for bottom clause construction, which is simpler but more expressive than the commonly used mode declarations; (2) a new covering technique, called super_covering, which reduces the examples in such a way that recursion can be learned, independently from the ordering of the examples; (3) a new search heuristics, called neg_coverage heuristics, which guides the search using only the number of negative examples covered by each hypothesis and (4) a new search algorithm, called doubly_guided_search, which searches for best clauses by alternating the use of two search heuristics, i.e. the traditional coverage search heuristics and the new neg_coverage search heuristics. The TWEETY system is shown to be more effective and efficient than the state-of-the-art ILP system ALEPH; the proposed techniques can be used to enhance efficiency and effectiveness of ALEPH and other systems based on the same ILP principles.
Year
DOI
Venue
2014
10.1080/0952813X.2013.808801
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE
Keywords
Field
DocType
logic programming,inductive logic programming,machine learning,efficiency
Declaration,Inductive logic programming,Search algorithm,Computer science,Theoretical computer science,Heuristics,Artificial intelligence,Logic programming,Machine learning,Recursion
Journal
Volume
Issue
ISSN
26.0
1
0952-813X
Citations 
PageRank 
References 
0
0.34
18
Authors
2
Name
Order
Citations
PageRank
Chongbing Liu1282.75
Enrico Pontelli21901181.26