Inductive logic programming (ILP) studies the learning of (Prolog) logic programs and other relational knowledge from examples. Most machine learning algorithms are restricted to finite, propositional ...
Complete implementation of Inductive Logic Programming algorithms with full research accuracy. Includes FOIL (Quinlan 1990) and Progol (Muggleton 1995) with comprehensive configuration. Muggleton, S. ...
Mechanistically interpretable neurosymbolic AI (Nature Comput Sci 2024): losslessly compressing NNs to computer code and discovering new algorithms which generalize out-of-distribution and outperform ...
Abstract: The Web has become an extremely large source of information and also a platform of various e-service including e-business, e-science, e-learning, e-government, etc. How to develop the new ...
Inductive logic programming [24] is situated in the intersection of machine learning or data mining on the one hand, and logic programming on the other hand. It shares with the former fields the goal ...
Abstract: Recently, there has been increasing interest in Inductive Logic Programming (ILP) systems. But existing ILP systems cannot improve themselves automatically. This paper describes an Adaptive ...
Comparative Results on Using Inductive Logic Programming for Corpus-based Parser Construction (1996)
This paper presents results from recent experiments with CHILL, a corpus-based parser acquisition system. CHILL treats language acquisition as the learning of search-control rules within a logic ...
ABSTRACT: This paper introduces a methodology that enables the relational learning framework to incorporate quantitative data derived from experimental studies in microbial ecology. The focus of using ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results