Paper Info
Title
Grammatical Concept Representation for Randomised Optimisation Algorithms in Relational Learning
Abstract
This paper proposes a novel grammar-based framework of concept representation for randomized search in Relational Learning (RL), namely for Inductive Logic Programming. The utilization of grammars guarantees that the search operations produce syntactically correct concepts and that the background knowledge encoded in the grammar can be used both for directing the search and for restricting the space of possible concepts to relevant candidate concepts (semantically valid concepts). Not only that it enables handling and incorporating the domain knowledge in a declarative fashion, but grammars also make the new approach transparent, flexible, less problem-specific and allow it to be easily used by almost any randomized algorithm within RL. Initial test results suggest that the grammar-based algorithm has strong potential for RL tasks.
Year
DOI
Venue
2009
10.1109/ISDA.2009.156
ISDA
Keywords
Field
DocType
rl task,grammars guarantee,grammar-based algorithm,randomised optimisation algorithms,relational learning,search operation,randomized search,randomized algorithm,grammatical concept representation,novel grammar-based framework,inductive logic programming,domain knowledge,evolutionary computation,reactive power,optimization,data mining,grammars,random search,learning artificial intelligence,logic programming
Rule-based machine translation,Statistical relational learning,Computer science,Natural language processing,Artificial intelligence,Logic programming,Inductive logic programming,Randomized algorithm,Domain knowledge,Algorithm,Evolutionary computation,Grammar,Machine learning
Conference
ISSN
Citations 
PageRank 
2164-7143
0
0.34
References 
Authors
14
3
Name
Order
Citations
PageRank
Petr Buryan192.18
Jirí Kubalík2116.50
Katsumi Inoue300.34