Area of research
Artificial Intelligence
Research interest
Research interests include Computer science, Probabilistic logic, Artificial intelligence, Programming language, Logic programming, and Probabilistic argumentation.
Proceedings 41st International Conference on Logic Programming
Most Probable Explanation in Probabilistic Answer Set Programming
Solving Decision Theory Problems with Probabilistic Answer Set Programming
Integrating Belief Domains into Probabilistic Logic Programs
Neurosymbolic AI for network intrusion detection systems: A survey
An Algebraic View of MAP Inference in Probabilistic Answer Set Programs
A Novel Framework for Reasoning over Optimization Problems in Probabilistic Answer Set Programming
A Neuro-Symbolic Artificial Intelligence Network Intrusion Detection System
Machine Learning Approaches for the Prediction of Gas Turbine Transients
Integration between constrained optimization and deep networks: a survey
Probabilistic Answer Set Programming with Discrete and Continuous Random Variables
Symbolic Parameter Learning in Probabilistic Answer Set Programming
Learning the Parameters of Probabilistic Answer Set Programs
Neuro-Symbolic Integration for Open Set Recognition in Network Intrusion Detection
Quantum algorithms for weighted constrained sampling and weighted model counting
Fast Inference for Probabilistic Answer Set Programs Via the Residual Program
MAP Inference in Probabilistic Answer Set Programs
Approximate Inference in Probabilistic Answer Set Programming for Statistical Probabilities
Automatic Differentiation in Prolog
Regularization in Probabilistic Inductive Logic Programming
Lifted inference for statistical statements in probabilistic answer set programming
Rapid Assessment of COVID-19 Mortality Risk with GASS Classifiers
Proceedings 39th International Conference on Logic Programming
Proceedings 39th International Conference on Logic Programming
Inference in Probabilistic Answer Set Programming Under the Credal Semantics
Foundations of Probabilistic Logic Programming
Proceedings 38th International Conference on Logic Programming
A Machine Learning Framework for Multi-Hazard Risk Assessment at the Regional Scale in Earthquake and Flood-Prone Areas
Statistical Statements in Probabilistic Logic Programming
Introduction to Machine Learning