Inductive Logic Programming 32nd International Conference, ILP 2023, Bari, Italy, November 13–15, 2023, Proceedings /
Inductive Logic Programming 32nd International Conference, ILP 2023, Bari, Italy, November 13–15, 2023, Proceedings / [electronic resource] :
edited by Elena Bellodi, Francesca Alessandra Lisi, Riccardo Zese.
- 1st ed. 2023.
- XVIII, 175 p. 40 illus., 35 illus. in color. online resource.
- Lecture Notes in Artificial Intelligence, 14363 2945-9141 ; .
- Lecture Notes in Artificial Intelligence, 14363 .
Declarative Sequential Pattern Mining in ASP -- Extracting Rules from ML models in Angluin’s Style -- A Constrained Optimization Approach to Set the Parameters of Probabilistic Answer Set Programs -- Regularization in Probabilistic Inductive Logic Programming -- Towards ILP-based LTLf passive learning -- Learning Strategies of Inductive Logic Programming Using Reinforcement Learning -- Select first, transfer later: choosing proper datasets for statistical relational transfer learning -- GNN based Extraction of Minimal Unsatisfiable Subsets -- What Do Counterfactuals Say about the World? Reconstructing Probabilistic Logic Programs from Answers to “What if?” Queries -- Few-shot learning of diagnostic rules for neurodegenerative diseases using Inductive Logic Programming -- An Experimental Overview of Neural-Symbolic Systems -- Statistical relational structure learning with scaled weight parameters -- A Review of Inductive Logic Programming Applications for Robotic Systems -- Meta Interpretive Learning from Fractal images.
This book constitutes the refereed proceedings of the 32nd International Conference on Inductive Logic Programming, ILP 2023, held in Bari, Italy, during November 13–15, 2023. The 11 full papers and 1 short paper included in this book were carefully reviewed and selected from 18 submissions. They cover all aspects of learning in logic, multi-relational data mining, statistical relational learning, graph and tree mining, learning in other (non-propositional) logic-based knowledge representation frameworks, exploring intersections to statistical learning and other probabilistic approaches.
9783031492990
10.1007/978-3-031-49299-0 doi
Artificial intelligence.
Computer engineering.
Computer networks .
Compilers (Computer programs).
Computer science.
Machine theory.
Artificial Intelligence.
Computer Engineering and Networks.
Compilers and Interpreters.
Computer Science Logic and Foundations of Programming.
Formal Languages and Automata Theory.
Q334-342 TA347.A78
006.3
Declarative Sequential Pattern Mining in ASP -- Extracting Rules from ML models in Angluin’s Style -- A Constrained Optimization Approach to Set the Parameters of Probabilistic Answer Set Programs -- Regularization in Probabilistic Inductive Logic Programming -- Towards ILP-based LTLf passive learning -- Learning Strategies of Inductive Logic Programming Using Reinforcement Learning -- Select first, transfer later: choosing proper datasets for statistical relational transfer learning -- GNN based Extraction of Minimal Unsatisfiable Subsets -- What Do Counterfactuals Say about the World? Reconstructing Probabilistic Logic Programs from Answers to “What if?” Queries -- Few-shot learning of diagnostic rules for neurodegenerative diseases using Inductive Logic Programming -- An Experimental Overview of Neural-Symbolic Systems -- Statistical relational structure learning with scaled weight parameters -- A Review of Inductive Logic Programming Applications for Robotic Systems -- Meta Interpretive Learning from Fractal images.
This book constitutes the refereed proceedings of the 32nd International Conference on Inductive Logic Programming, ILP 2023, held in Bari, Italy, during November 13–15, 2023. The 11 full papers and 1 short paper included in this book were carefully reviewed and selected from 18 submissions. They cover all aspects of learning in logic, multi-relational data mining, statistical relational learning, graph and tree mining, learning in other (non-propositional) logic-based knowledge representation frameworks, exploring intersections to statistical learning and other probabilistic approaches.
9783031492990
10.1007/978-3-031-49299-0 doi
Artificial intelligence.
Computer engineering.
Computer networks .
Compilers (Computer programs).
Computer science.
Machine theory.
Artificial Intelligence.
Computer Engineering and Networks.
Compilers and Interpreters.
Computer Science Logic and Foundations of Programming.
Formal Languages and Automata Theory.
Q334-342 TA347.A78
006.3