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Fabrizio Riguzzi

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

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

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  • More about Foundations of Probabilistic Logic Programming: Languages, Semantics, Inference and Learning


Probabilistic Logic Programming has seen a steady increase of activity, with many proposals for languages and algorithms for inference and learning. This 2nd edition aims to report the most exciting novelties in the field since the publication of the 1st edition, including semantics for hybrid programs, Probabilistic Answer Set Programming, algorithms for solving the MPE and MAP tasks, and neuro-symbolic integration.

Format: Hardback
Length: 506 pages
Publication date: 07 July 2023
Publisher: River Publishers


The field of Probabilistic Logic Programming has experienced a remarkable surge in activity since its inception, with a multitude of proposals for languages and algorithms for inference and learning. This comprehensive book aims to provide a comprehensive overview of the field, with a special focus on languages under the Distribution Semantics approach, one of the most influential paradigms. The book presents the fundamental concepts of semantics, inference, and learning, while also highlighting the connections between these methods. Numerous examples throughout the book include a link to a web application http://cplint.eu, where the code can be executed online. The 2nd edition of this book endeavors to report the most exciting novelties in the field since the publication of the 1st edition. The semantics for hybrid programs with function symbols has been firmly established, while Probabilistic Answer Set Programming has gained significant attention alongside the studies on inference complexity. Algorithms for solving the Minimum Perturbation and Maximum A Posteriori tasks are now readily available. Inference for hybrid programs has undergone a dramatic transformation with the introduction of Weighted Model Integration.

With respect to learning, the first approaches for neuro-symbolic integration have emerged, accompanied by algorithms for learning the structure of hybrid programs. Moreover, in light of the cost associated with learning Probabilistic Logic Programs, various works have proposed language restrictions to enhance learning speed and scalability.

Weight: 1170g
Dimension: 234 x 156 (mm)
ISBN-13: 9788770227193
Edition number: 2 ed

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