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Statements

Subject Item
n2:RIV%2F00216208%3A11320%2F13%3A10194756%21RIV14-GA0-11320___
rdf:type
n8:Vysledek skos:Concept
rdfs:seeAlso
http://dx.doi.org/10.1007/s10601-013-9141-7
dcterms:description
Inductive Logic Programming (ILP) deals with the problem of finding a hypothesis covering positive examples and excluding negative examples, where both hypotheses and examples are expressed in first-order logic. In this paper we employ constraint satisfaction techniques to model and solve a problem known as template ILP consistency, which assumes that the structure of a hypothesis is known and the task is to find unification of the contained variables. In particular, we present a constraint model with index variables accompanied by a Boolean model to strengthen inference and hence improve efficiency. The efficiency of models is demonstrated experimentally. Inductive Logic Programming (ILP) deals with the problem of finding a hypothesis covering positive examples and excluding negative examples, where both hypotheses and examples are expressed in first-order logic. In this paper we employ constraint satisfaction techniques to model and solve a problem known as template ILP consistency, which assumes that the structure of a hypothesis is known and the task is to find unification of the contained variables. In particular, we present a constraint model with index variables accompanied by a Boolean model to strengthen inference and hence improve efficiency. The efficiency of models is demonstrated experimentally.
dcterms:title
Formulating the template ILP consistency problem as a constraint satisfaction problem Formulating the template ILP consistency problem as a constraint satisfaction problem
skos:prefLabel
Formulating the template ILP consistency problem as a constraint satisfaction problem Formulating the template ILP consistency problem as a constraint satisfaction problem
skos:notation
RIV/00216208:11320/13:10194756!RIV14-GA0-11320___
n8:predkladatel
n13:orjk%3A11320
n3:aktivita
n12:I n12:P
n3:aktivity
I, P(GAP103/10/1875), P(GBP202/12/G061)
n3:cisloPeriodika
2
n3:dodaniDat
n19:2014
n3:domaciTvurceVysledku
n4:8318573
n3:druhVysledku
n10:J
n3:duvernostUdaju
n7:S
n3:entitaPredkladatele
n20:predkladatel
n3:idSjednocenehoVysledku
75555
n3:idVysledku
RIV/00216208:11320/13:10194756
n3:jazykVysledku
n17:eng
n3:klicovaSlova
Meta-reasoning; Inductive logic programming; Constraint modeling
n3:klicoveSlovo
n5:Constraint%20modeling n5:Meta-reasoning n5:Inductive%20logic%20programming
n3:kodStatuVydavatele
NL - Nizozemsko
n3:kontrolniKodProRIV
[2E8A670142F0]
n3:nazevZdroje
Constraints
n3:obor
n18:JC
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
4
n3:projekt
n15:GBP202%2F12%2FG061 n15:GAP103%2F10%2F1875
n3:rokUplatneniVysledku
n19:2013
n3:svazekPeriodika
18
n3:tvurceVysledku
Cernoch, Radomir Barták, Roman Železný, Filip Kuzelka, Ondrej
n3:wos
000316021000002
s:issn
1383-7133
s:numberOfPages
22
n16:doi
10.1007/s10601-013-9141-7
n6:organizacniJednotka
11320