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 is placed on a sound footing.

The integration of logic and probability combines the capability of the first to represent complex relations among entities with the capability of the latter to model uncertainty over attributes and relations. Logic programming provides a Turing complete language based on logic and thus represent an excellent candidate for the integration. 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. One of most successful approaches to Probabilistic Logic Programming is the Distribution Semantics, where a probabilistic logic program defines a probability distribution over normal logic programs and the probability of a ground query is then obtained from the joint distribution of the query and the programs. Foundations of Probabilistic Logic Programming aims at providing an overview of the field of Probabilistic Logic Programming, with a special emphasis on languages under the Distribution Semantics. 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 book constitutes the refereed conference proceedings of the 28th International Conference on Inductive Logic Programming, ILP 2018, held in Ferrara, Italy, in September 2018. The 10 full papers presented were carefully reviewed and selected from numerous submissions. Inductive Logic Programming (ILP) is a subfield of machine learning, which originally relied on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data.

This book constitutes the thoroughly refereed post-proceedings of the 22nd International Conference on Inductive Logic Programming, ILP 2012, held in Dubrovnik, Croatia, in September 2012. The 18 revised full papers were carefully reviewed and selected from 41 submissions. The papers cover the following topics: propositionalization, logical foundations, implementations, probabilistic ILP, applications in robotics and biology, grammatical inference, spatial learning and graph-based learning.

This book constitutes the refereed proceedings of the 14th International Conference of the Italian Association for Artificial Intelligence, A*IA 2015, held in Ferrara, Italy, in September 2015. The 35 full papers presented were carefully reviewed and selected from 44 submissions. The papers are organized in topical sections on swarm intelligence and genetic algorithms; computer vision; multi-agents systems; knowledge representation and reasoning; machine learning; semantic Web; natural language; and scheduling, planning and robotics.