Learning probabilistic logic models from probabilistic examples

DOI10.1007/s10994-008-5076-4zbMath1470.68093OpenAlexW2009141112WikidataQ38377911 ScholiaQ38377911MaRDI QIDQ1009281

José Santos, Jianzhong Chen, Stephen H. Muggleton

Publication date: 31 March 2009

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-008-5076-4

zbMATH Keywords

abductionstochastic logic programsmetabolic networksprobabilistic inductive logic programmingprobabilistic examples

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Logic programming (68N17) Systems biology, networks (92C42)

Related Items

Constraint-based probabilistic modeling for statistical abduction ⋮ ILP turns 20. Biography and future challenges

Uses Software

• YAP-Prolog

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• An analysis of first-order logics of probability
• A universal prior for integers and estimation by minimum description length
• Probabilistic Horn abduction and Bayesian networks
• The independent choice logic for modelling multiple agents under uncertainty
• Application of abductive ILP to learning metabolic network inhibition from temporal data
• Probabilistic inductive logic programming. Theory and applications
• Markov logic networks
• Inductive Logic Programming: Theory and methods
• Abductive Logic Programming
• Algorithmic Learning Theory
• Parameter estimation in stochastic logic programs