Foundations of Probabilistic Logic Programming
Languages, Semantics, Inference and Learning
Second edition, 2022
Author: Fabrizio Riguzzi, University of Ferrara, Italy
Publisher: River Publishers
Series: River Publishers Series in Software Engineering
ISBN: 9788770227063
e-ISBN: 9788770227193
https://ml.unife.it/plp-book/
The book is now available from IEEE Xplore at https://ieeexplore.ieee.org/book/9999514
It will be available in print and e-book form in June 2023.
Abstract
Probabilistic logic programming extends logic programming by enabling the representation of uncertain information by means of probability theory. Probabilistic logic programming is at the intersection of two wider research fields: the integration of logic and probability and probabilistic programming.Logic enables the representation of complex relations among entities while probability theory is useful for modeling uncertainty over attributes and relations. Combining the two is a very active field of study.Probabilistic programming extends programming languages with probabilistic primitives that can be used to write complex probabilistic models. Algorithms for inference and learning tasks are then provided automatically by the system. Probabilistic logic programming is at the same time a logic language, with its knowledge representation capabilities, and a Turing complete language, with its computation capabilities, thus providing the best of both worlds.
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 between the methods.Many examples of the book include a link to a page of the web application http://cplint.eu where the code can be run online.
This 2nd edition aims at reporting the most exciting novelties in the field since the publication of the 1st edition. The semantics for hybrid programs with function symbols was placed on a sound footing. Probabilistic Answer Set Programming gained a lot of interest together with studies on the complexity of inference. Algorithms for solving the MPE and MAP tasks are now available. Inference for hybrid programs has changed dramatically with the introduction of Weighted Model Integration.
With respect to learning, the first approaches for neuro-symbolic integration have appeared together with algorithms for learning the structure of hybrid programs
Moreover, given the cost of learning PLPs, various works proposed language restrictions to speed up learning and improve its scaling.
Keywords:
Probabilistic logic programming, statistical relational learning, statistical relational artificial intelligence, distribution semantics, graphical models, artificial intelligence, machine learning
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