Foundations of Probabilistic Logic Programming: Languages, Semantics, Inference and Learning [Hardcover]

1. Preliminaries 2. Probabilistic Logic Programming Languages 3. Semantics with Function Symbols 4. Hybrid Programs 5. Semantics for Hybrid Programs with Function Symbols 6. Probabilistic Answer Set Programming 7. Complexity of Inference 8. Exact Inference 9. Lifted Inference 10. Approximate Inference 11. Non-Standard Inference 12. Inference for Hybrid Programs 13. Parameter Learning 14. Structure Learning 15. cplint Examples 16. Conclusions

This book aims at providing an overview of probabilistic logic programming with a special emphasis on languages under the distribution semantics, and presents the main ideas for semantics, inference, and learning and highlights connections between the methods.

Fabrizio Riguzzi is Full Professor of Computer Science at the Department of Mathematics and Computer Science of the University of Ferrara. He was previously Associate Professor and Assistant Professor at the same university. He obtained his Masters and PhD in Computer Engineering from the University of Bologna. Fabrizio Riguzzi is Editor in Chief of Intelligenza Artificiale, the official journal of the Italian Association for Artificial Intelligence. He is the author of more than 200 peer reviewed papers in the areas of machine learning, inductive logic programming and statistical relational learning. His aim is to develop intelligent systems by combining in novel ways techniques from artificial intelligence, logic and statistics.

Since its birth, the field of Probabilistic Logic Programming has seen a steady increase of activity, with many proposals for languages and algorithms for inference and learning.

This book aims at providing an overview of the field with a special emphasis on languages under the Distribution Semantics, one of the most influential approaches. The book presents the main ideas for semantics, inference, and learning and highlights connections lӝ