Inference and learning algorithms for probabilistic logic programming

• Grant number: 17/19007-6
• Support Opportunities: Scholarships in Brazil - Master
• Start date: October 01, 2017
• End date: November 30, 2018
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Agreement: IBM Brasil
• Principal Investigator: Fabio Gagliardi Cozman
• Grantee: Arthur Colombini Gusmão
• Host Institution: Escola Politécnica (EP). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Company: Universidade de São Paulo (USP). Escola Politécnica (EP)
• Associated research grant: 16/18841-0 - Inference and learning algorithms for probabilistic logic programming, AP.PITE

Abstract

The goal of this project is to develop inference and learning techniques for proba- bilistic logic programs, with an eye on the scalable automatic induction of proba- bilistic rules from large datasets. Such techniques have applications in information search and retrieval, automated diagnosis, decision and recommendation systems - applications that benefit from large and accurate knowledge bases. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

DE FARIA, FRANCISCO H. O. VIEIRA; GUSMAO, ARTHUR COLOMBINI; DE BONA, GLAUBER; MAUA, DENIS DERATANI; COZMAN, FABIO GAGLIARDI. Speeding up parameter and rule learning for acyclic probabilistic logic programs. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, v. 106, p. 32-50, MAR 2019. (17/19007-6, 16/18841-0, 16/25928-4, 16/01055-1, 15/21880-4)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

GUSMÃO, Arthur Colombini. Interpreting embedding models of knowledge bases.. 2018. Master's Dissertation - Universidade de São Paulo (USP). Escola Politécnica (EP/BC) São Paulo.