Learning Theories Using Estimation Distribution Algorithms and (Reduced) Bottom Clauses

By C. Pitangui, Gerson Zaverucha. 2011

13 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. A.S. d'Avila Garcez
2. Luciano Serafini
3. L. C. Lamb
4. Michael Spranger
5. Son Dinh Tran
6. Marco Gori
7. William L. Hamilton
8. Josh Tenenbaum

In Computational Theory and Mathematics

1. A.S. d'Avila Garcez
2. Luciano Serafini
3. Marco Gori
4. Michael Spranger
5. Son Dinh Tran
6. L. C. Lamb

In Information Systems

1. Luciano Serafini
2. Michael Spranger
3. Son Dinh Tran
4. L. C. Lamb
5. A.S. d'Avila Garcez
6. Marco Gori

Citations in the corpus

• Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning (2019, methodology): “In [36], prositionalisation is used for learning first-order logic in Bayesian networks.”
• Fast relational learning using bottom clause propositionalization with artificial neural networks (2013, unknown): “…clauses for propositionalization because they carry semantic meaning, and because bottom clause literals can be used directly as features in a truth-table, simplifying the feature extraction process (Muggleton and Tamaddoni-Nezhad, 2008; DiMaio and Shavlik, 2004; Pitangui and Zaverucha, 2012).”