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» Learning Stochastic Logic Programs
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JMLR
2006
100views more  JMLR 2006»
13 years 7 months ago
Learning Recursive Control Programs from Problem Solving
In this paper, we propose a new representation for physical control
Pat Langley, Dongkyu Choi
ILP
2000
Springer
13 years 11 months ago
Bayesian Logic Programs
First-order probabilistic models are recognized as efficient frameworks to represent several realworld problems: they combine the expressive power of first-order logic, which serv...
Kristian Kersting, Luc De Raedt
FLAIRS
2007
13 years 10 months ago
Managing Dynamic Contexts Using Failure-Driven Stochastic Models
We describe an architecture for representing and managing context shifts that supports dynamic data interpretation. This architecture utilizes two layers of learning and three lay...
Nikita A. Sakhanenko, George F. Luger, Carl R. Ste...
ICML
1989
IEEE
13 years 11 months ago
Higher-Order and Modal Logic as a Framework for Explanation-Based Generalization
Logic programming provides a uniform framework in which all aspects of explanation-based generalization and learning may be defined and carried out, but first-order Horn logic i...
Scott Dietzen, Frank Pfenning
ICML
1994
IEEE
13 years 11 months ago
Combining Top-down and Bottom-up Techniques in Inductive Logic Programming
This paper describes a new methodfor inducing logic programs from examples which attempts to integrate the best aspects of existingILP methodsintoa singlecoherent framework. In pa...
John M. Zelle, Raymond J. Mooney, Joshua B. Konvis...