Towards Probabilistic Inductive Logic Programming with Neurosymbolic Inference and Relaxation

Many inductive logic programming (ILP) methods are incapable of learning programs from probabilistic background knowledge, for example, coming from sensory data or neural networks with probabilities. We propose Propper, which handles flawed and probabilistic background knowledge by extending ILP wit...

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Veröffentlicht in:	Theory and practice of logic programming Jg. 24; H. 4; S. 628 - 643
Hauptverfasser:	HILLERSTRÖM, FIEKE, BURGHOUTS, GERTJAN
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cambridge, UK Cambridge University Press 01.07.2024
Schlagworte:	Artificial intelligence Background noise Deep learning Graph neural networks Hypotheses Hypothesis testing Knowledge Learning programs Logic programming Methods Neural networks Original Article Probabilistic inference Probability Statistical analysis Statistical models neurosymbolic inference probabilistic background knowledge sensory data relational patterns inductive logic programming
ISSN:	1471-0684, 1475-3081
Online-Zugang:	Volltext
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