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 the 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 for hybrid programs. Moreover, given the cost of learning PLPs, various works proposed language restrictions to speed up learning and improve its scaling.

This book provides an overview of the theoretical underpinnings of modern probabilistic programming and presents applications in e.g., machine learning, security, and approximate computing. Comprehensive survey chapters make the material accessible to graduate students and non-experts. This title is also available as Open Access on Cambridge Core.

While probabilistic logics in principle might be applied to solve a range of problems, in practice they are rarely applied - perhaps because they seem disparate, complicated, and computationally intractable. This programmatic book argues that several approaches to probabilistic logic fit into a simple unifying framework in which logically complex evidence is used to associate probability intervals or probabilities with sentences. Specifically, Part I shows that there is a natural way to present a question posed in probabilistic logic, and that various inferential procedures provide semantics for that question, while Part II shows that there is the potential to develop computationally feasible methods to mesh with this framework. The book is intended for researchers in philosophy, logic, computer science and statistics. A familiarity with mathematical concepts and notation is presumed, but no advanced knowledge of logic or probability theory is required.

The present study is an extension of the topic introduced in Dr. Hailperin's Sentential Probability Logic, where the usual true-false semantics for logic is replaced with one based more on probability, and where values ranging from 0 to 1 are subject to probability axioms. Moreover, as the word "sentential" in the title of that work indicates, the language there under consideration was limited to sentences constructed from atomic (not inner logical components) sentences, by use of sentential connectives ("no," "and," "or," etc.) but not including quantifiers ("for all," "there is"). An initial introduction presents an overview of the book. In chapter one, Halperin presents a summary of results from his earlier book, some of which extends into this work. It also contains a novel treatment of the problem of combining evidence: how does one combine two items of interest for a conclusion-each of which separately impart a probability for the conclusion-so as to have a probability for the conclusion basedon taking both of the two items of interest as evidence? Chapter two enlarges the Probability Logic from the first chapter in two respects: the language now includes quantifiers ("for all," and "there is") whose variables range over atomic sentences, notentities as with standard quantifier logic. (Hence its designation: ontological neutral logic.) A set of axioms for this logic is presented. A new sentential notion-the suppositional-in essence due to Thomas Bayes, is adjoined to this logic that later becomes the basis for creating a conditional probability logic. Chapter three opens with a set of four postulates for probability on ontologically neutral quantifier language. Many properties are derived and a fundamental theorem is proved, namely, for anyprobability model (assignment of probability values to all atomic sentences of the language) there will be a unique extension of the probability values to all closed sentences of the language. The chapter concludes by showing the Borel's early denumerableprobability concept (1909) can be justified by its being, in essence, close to Hailperin's probability result applied to denumerable language. The final chapter introduces the notion of conditional-probability to a language having quantifiers of the kind

This book constitutes the refereed proceedings of the 20th International Workshop on Computer Science Logic, CSL 2006, held as the 15th Annual Conference of the EACSL in Szeged, Hungary in September 2006. The 37 revised full papers presented together with 4 invited contributions were carefully reviewed and selected from 132 submissions. All current aspects of logic in computer science are addressed, including automated deduction and interactive theorem proving, constructive mathematics and type theory, equational logic and term rewriting, automata and formal logics, modal and temporal logic, model checking, logical aspects of computational complexity, finite model theory, computational proof theory, logic programming and constraints, lambda calculus and combinatory logic, categorical logic and topological semantics, domain theory, database theory, specification, extraction and transformation of programs, logical foundations of programming paradigms, verification of security protocols, linear logic, higher-order logic, nonmonotonic reasoning, as well as logics and type systems for biology.

This book constitutes the thoroughly refereed post-proceedings of the 29th Annual German Conference on Artificial Intelligence, KI 2006, held in Bremen, Germany, in June 2006. This was co-located with RoboCup 2006, the innovative robot soccer world championship, and with ACTUATOR 2006, the 10th International Conference on New Actuators. The 29 revised full papers presented together with two invited contributions were carefully reviewed and selected from 112 submissions.

The Annual Conference of the European Association for Computer Science Logic, CSL 2002, was held in the Old College of the University of Edinburgh on 22–25 September 2002. The conference series started as a programme of Int- national Workshops on Computer Science Logic, and then in its sixth meeting became the Annual Conference of the EACSL. This conference was the sixteenth meeting and eleventh EACSL conference; it was organized by the Laboratory for Foundations of Computer Science at the University of Edinburgh. The CSL 2002 Programme Committee considered 111 submissions from 28 countries during a two week electronic discussion; each paper was refereed by at least three reviewers. The Committee selected 37 papers for presentation at the conference and publication in these proceedings. The Programme Committee invited lectures from Susumu Hayashi, Frank Neven, and Damian Niwinski; ́ the papers provided by the invited speakers appear at the front of this volume. In addition to the main conference, two tutorials – ‘Introduction to Mu- Calculi’ (Julian Brad?eld) and ‘Parametrized Complexity’ (Martin Grohe) – were given on the previous day.

Column Generation is an insightful overview of the state of the art in integer programming column generation and its many applications. The volume begins with "A Primer in Column Generation" which outlines the theory and ideas necessary to solve large-scale practical problems, illustrated with a variety of examples. Other chapters follow this introduction on "Shortest Path Problems with Resource Constraints," "Vehicle Routing Problem with Time Window," "Branch-and-Price Heuristics," "Cutting Stock Problems," each dealing with methodological aspects of the field. Three chapters deal with transportation applications: "Large-scale Models in the Airline Industry," "Robust Inventory Ship Routing by Column Generation," and "Ship Scheduling with Recurring Visits and Visit Separation Requirements." Production is the focus of another three chapters: "Combining Column Generation and Lagrangian Relaxation," "Dantzig-Wolfe Decomposition for Job Shop Scheduling," and "Applying Column Generation to Machine Scheduling." The final chapter by François Vanderbeck, "Implementing Mixed Integer Column Generation," reviews how to set-up the Dantzig-Wolfe reformulation, adapt standard MIP techniques to the column generation context (branching, preprocessing, primal heuristics), and deal with specific column generation issues (initialization, stabilization, column management strategies).

FoLLI-LNCS is the publication platform for the Association of Logic, Language and Information (FoLLI, www.folli.org). The Association was founded in 1991 to advance research and education on the interface between logic, linguistics, computer science, and cognitive science. The FoLLI Publications on Logic, Language and Information aim to disseminate results of cutting-edge research and tutorial materials in these interdisciplinary areas. This LNCS volume is part of FoLLi book serie and contains the papers presented at the 5th International Workshop on Logic, Rationality and Interaction/ (LORI-V), held in October 2015 in Taipei, Taiwan. The topics covered in this program well represent the span and depth that hasby now become a trademark of the LORI workshop series, where logic interfaceswith disciplines as diverse as game theory and decision theory, philosophyand epistemology, linguistics, computer science and artificial intelligence.

Topics covered: Theoretical Foundations. Higher-Order Logics. Non-Monotonic Reasoning. Programming Methodology. Programming Environments. Extensions to Logic Programming. Constraint Satisfaction. Meta-Programming. Language Design and Constructs. Implementation of Logic Programming Languages. Compilation Techniques. Architectures. Parallelism. Reasoning about Programs. Deductive Databases. Applications. 13-16 June 1995, Tokyo, Japan ICLP, which is sponsored by the Association for Logic Programming, is one of two major annual international conferences reporting recent research results in logic programming. Logic programming originates from the discovery that a subset of predicate logic could be given a procedural interpretation which was first embodied in the programming language, Prolog. The unique features of logic programming make it appealing for numerous applications in artificial intelligence, computer-aided design and verification, databases, and operations research, and for exploring parallel and concurrent computing. The last two decades have witnessed substantial developments in this field from its foundation to implementation, applications, and the exploration of new language designs. Topics covered: Theoretical Foundations. Higher-Order Logics. Non-Monotonic Reasoning. Programming Methodology. Programming Environments. Extensions to Logic Programming. Constraint Satisfaction. Meta-Programming. Language Design and Constructs. Implementation of Logic Programming Languages. Compilation Techniques. Architectures. Parallelism. Reasoning about Programs. Deductive Databases. Applications. Logic Programming series, Research Reports and Notes