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» An Introduction to Inductive Logic Programming and Learning ...
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UAI
2003
13 years 8 months ago
CLP(BN): Constraint Logic Programming for Probabilistic Knowledge
Abstract. In Datalog, missing values are represented by Skolem constants. More generally, in logic programming missing values, or existentially quantified variables, are represent...
Vítor Santos Costa, David Page, Maleeha Qaz...
ILP
1998
Springer
13 years 11 months ago
Strongly Typed Inductive Concept Learning
In this paper we argue that the use of a language with a type system, together with higher-order facilities and functions, provides a suitable basis for knowledge representation in...
Peter A. Flach, Christophe G. Giraud-Carrier, John...
CORR
2000
Springer
120views Education» more  CORR 2000»
13 years 7 months ago
Scaling Up Inductive Logic Programming by Learning from Interpretations
When comparing inductive logic programming (ILP) and attribute-value learning techniques, there is a trade-off between expressive power and efficiency. Inductive logic programming ...
Hendrik Blockeel, Luc De Raedt, Nico Jacobs, Bart ...
ICALP
2007
Springer
14 years 1 months ago
Co-Logic Programming: Extending Logic Programming with Coinduction
In this paper we present the theory and practice of co-logic programming (co-LP for brevity), a paradigm that combines both inductive and coinductive logic programming. Co-LP is a ...
Luke Simon, Ajay Bansal, Ajay Mallya, Gopal Gupta
ICTAI
2007
IEEE
14 years 1 months ago
ExOpaque: A Framework to Explain Opaque Machine Learning Models Using Inductive Logic Programming
In this paper we developed an Inductive Logic Programming (ILP) based framework ExOpaque that is able to extract a set of Horn clauses from an arbitrary opaque machine learning mo...
Yunsong Guo, Bart Selman