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Probabilistic Constraint Logic Programming


Probabilistic Constraint Logic Programming
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Probabilistic Constraint Logic Programming


Probabilistic Constraint Logic Programming
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Author : Stefan Riezler
language : en
Publisher:
Release Date : 1999

Probabilistic Constraint Logic Programming written by Stefan Riezler and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Constraint programming (Computer science) categories.




Foundations Of Probabilistic Logic Programming


Foundations Of Probabilistic Logic Programming
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Author : Fabrizio Riguzzi
language : en
Publisher: CRC Press
Release Date : 2023-07-07

Foundations Of Probabilistic Logic Programming written by Fabrizio Riguzzi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-07 with Computers categories.


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.



Constraint Satisfaction In Logic Programming


Constraint Satisfaction In Logic Programming
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Author : Pascal Van Hentenryck
language : en
Publisher: MIT Press (MA)
Release Date : 1989

Constraint Satisfaction In Logic Programming written by Pascal Van Hentenryck and has been published by MIT Press (MA) this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989 with Computers categories.


This book tackles classic problems from operations research and circuit design using a logic programming language embedding consistency techniques, a paradigm emerging from artificial intelligence research. Van Hentenryck proposes a new approach to solving discrete combinatorial problems using these techniques.Logic programming serves as a convenient language for stating combinatorial problems, but its "generate and test" paradigm leads to inefficient programs. Van Hentenryck's approach preserves one of the most useful features of logic programming - the duality of its semantics - yet allows a short development time for the programs while preserving most of the efficiency of special purpose programs written in a procedural language.Embedding consistency techniques in logic programming allows for ease and flexibility of programming and short development time because constraint propagation and tree-search programming are abstracted away from the user. It also enables logic programs to be executed efficiently as consistency techniques permit an active use of constraints to remove combinations of values that cannot appear in a solution Van Hentenryck presents a comprehensive overview of this new approach from its theoretical foundations to its design and implementation, including applications to real life combinatorial problems.The ideas introduced in "Constraint Satisfaction in Logic Programming "have been used successfully to solve more than a dozen practical problems in operations research and circuit design, including disjunctive scheduling, warehouse location, cutting stock car sequencing, and microcode labeling problems.Pascal Van Hentenryck is a member of the research staff at the European Computer Industry Research Centre. "Constraint Satisfaction in Logic Programming" is based on research for the Centre's CHIP project. As an outgrowth of this project, a new language (CHIP) that will include consistency techniques has been developed for commercial use. The book is included in the Logic Programming series edited by Ehud Shapiro.



Foundations Of Probabilistic Logic Programming


Foundations Of Probabilistic Logic Programming
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Author : Fabrizio Riguzzi
language : en
Publisher: River Publishers
Release Date : 2018-09-01

Foundations Of Probabilistic Logic Programming written by Fabrizio Riguzzi and has been published by River Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-01 with Computers categories.


Probabilistic Logic Programming extends Logic Programming by enabling the representation of uncertain information. 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 model 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 the 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. Foundations of Probabilistic Logic Programming 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.



Probabilistic Inductive Logic Programming


Probabilistic Inductive Logic Programming
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Author : Luc De Raedt
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-03-14

Probabilistic Inductive Logic Programming written by Luc De Raedt and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-03-14 with Computers categories.


The question, how to combine probability and logic with learning, is getting an increased attention in several disciplines such as knowledge representation, reasoning about uncertainty, data mining, and machine learning simulateously. This results in the newly emerging subfield known under the names of statistical relational learning and probabilistic inductive logic programming. This book provides an introduction to the field with an emphasis on the methods based on logic programming principles. It is concerned with formalisms and systems, implementations and applications, as well as with the theory of probabilistic inductive logic programming. The 13 chapters of this state-of-the-art survey start with an introduction to probabilistic inductive logic programming; moreover the book presents a detailed overview of the most important probabilistic logic learning formalisms and systems such as relational sequence learning techniques, using kernels with logical representations, Markov logic, the PRISM system, CLP(BN), Bayesian logic programs, and the independent choice logic. The third part provides a detailed account of some show-case applications of probabilistic inductive logic programming. The final part touches upon some theoretical investigations and includes chapters on behavioural comparison of probabilistic logic programming representations and a model-theoretic expressivity analysis.



Inductive Logic Programming


Inductive Logic Programming
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Author : James Cussens
language : en
Publisher: Springer Science & Business Media
Release Date : 2000-07-12

Inductive Logic Programming written by James Cussens and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-07-12 with Computers categories.


Mich`eleSebag(EcolePolytechnique,France) AshwinSrinivasan(UniversityofOxford,UK) PrasadTadepalli(OregonStateUniversity,USA) StefanWrobel(UniversityofMagdeburg,Germany) AkihiroYamamoto(UniversityofHokkaido,Japan) Additional Referees ́ ErickAlphonse(Universit ́edeParis-Sud,France) LiviuBadea(NationalInstituteforResearchandDevelopmentinInformatics,



Computational Logic Cl 2000


Computational Logic Cl 2000
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Author : John Lloyd
language : en
Publisher: Springer
Release Date : 2003-06-26

Computational Logic Cl 2000 written by John Lloyd and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-06-26 with Computers categories.


These are the proceedings of the First International Conference on Compu- tional Logic (CL 2000) which was held at Imperial College in London from 24th to 28th July, 2000. The theme of the conference covered all aspects of the theory, implementation, and application of computational logic, where computational logic is to be understood broadly as the use of logic in computer science. The conference was collocated with the following events: { 6th International Conference on Rules and Objects in Databases (DOOD 2000) { 10th International Workshop on Logic-based Program Synthesis and Tra- formation (LOPSTR 2000) { 10th International Conference on Inductive Logic Programming (ILP 2000). CL 2000 consisted of seven streams: { Program Development (LOPSTR 2000) { Logic Programming: Theory and Extensions { Constraints { Automated Deduction: Putting Theory into Practice { Knowledge Representation and Non-monotonic Reasoning { Database Systems (DOOD 2000) { Logic Programming: Implementations and Applications. The LOPSTR 2000 workshop constituted the program development stream and the DOOD 2000 conference constituted the database systems stream. Each stream had its own chair and program committee, which autonomously selected the papers in the area of the stream. Overall, 176 papers were submitted, of which 86 were selected to be presented at the conference and appear in these proceedings. The acceptance rate was uniform across the streams. In addition, LOPSTR 2000 accepted about 15 extended abstracts to be presented at the conference in the program development stream.



Declarative Logic Programming


Declarative Logic Programming
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Author : Michael Kifer
language : en
Publisher: Morgan & Claypool
Release Date : 2018-09-19

Declarative Logic Programming written by Michael Kifer and has been published by Morgan & Claypool this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-19 with Computers categories.


The idea of this book grew out of a symposium that was held at Stony Brook in September 2012 in celebration of David S.Warren's fundamental contributions to Computer Science and the area of Logic Programming in particular. Logic Programming (LP) is at the nexus of Knowledge Representation, Artificial Intelligence, Mathematical Logic, Databases, and Programming Languages. It is fascinating and intellectually stimulating due to the fundamental interplay among theory, systems, and applications brought about by logic. Logic programs are more declarative in the sense that they strive to be logical specifications of "what" to do rather than "how" to do it, and thus they are high-level and easier to understand and maintain. Yet, without being given an actual algorithm, LP systems implement the logical specifications automatically. Several books cover the basics of LP but focus mostly on the Prolog language with its incomplete control strategy and non-logical features. At the same time, there is generally a lack of accessible yet comprehensive collections of articles covering the key aspects in declarative LP. These aspects include, among others, well-founded vs. stable model semantics for negation, constraints, object-oriented LP, updates, probabilistic LP, and evaluation methods, including top-down vs. bottom-up, and tabling. For systems, the situation is even less satisfactory, lacking accessible literature that can help train the new crop of developers, practitioners, and researchers. There are a few guides onWarren’s Abstract Machine (WAM), which underlies most implementations of Prolog, but very little exists on what is needed for constructing a state-of-the-art declarative LP inference engine. Contrast this with the literature on, say, Compilers, where one can first study a book on the general principles and algorithms and then dive in the particulars of a specific compiler. Such resources greatly facilitate the ability to start making meaningful contributions quickly. There is also a dearth of articles about systems that support truly declarative languages, especially those that tie into first-order logic, mathematical programming, and constraint solving. LP helps solve challenging problems in a wide range of application areas, but in-depth analysis of their connection with LP language abstractions and LP implementation methods is lacking. Also, rare are surveys of challenging application areas of LP, such as Bioinformatics, Natural Language Processing, Verification, and Planning. The goal of this book is to help fill in the previously mentioned void in the LP literature. It offers a number of overviews on key aspects of LP that are suitable for researchers and practitioners as well as graduate students. The following chapters in theory, systems, and applications of LP are included.



Encyclopedia Of Optimization


Encyclopedia Of Optimization
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Author : Christodoulos A. Floudas
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-09-04

Encyclopedia Of Optimization written by Christodoulos A. Floudas and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-09-04 with Mathematics categories.


The goal of the Encyclopedia of Optimization is to introduce the reader to a complete set of topics that show the spectrum of research, the richness of ideas, and the breadth of applications that has come from this field. The second edition builds on the success of the former edition with more than 150 completely new entries, designed to ensure that the reference addresses recent areas where optimization theories and techniques have advanced. Particularly heavy attention resulted in health science and transportation, with entries such as "Algorithms for Genomics", "Optimization and Radiotherapy Treatment Design", and "Crew Scheduling".



Foundations Of Bayesianism


Foundations Of Bayesianism
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Author : D. Corfield
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-14

Foundations Of Bayesianism written by D. Corfield and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-14 with Science categories.


Foundations of Bayesianism is an authoritative collection of papers addressing the key challenges that face the Bayesian interpretation of probability today. Some of these papers seek to clarify the relationships between Bayesian, causal and logical reasoning. Others consider the application of Bayesianism to artificial intelligence, decision theory, statistics and the philosophy of science and mathematics. The volume includes important criticisms of Bayesian reasoning and also gives an insight into some of the points of disagreement amongst advocates of the Bayesian approach. The upshot is a plethora of new problems and directions for Bayesians to pursue. The book will be of interest to graduate students or researchers who wish to learn more about Bayesianism than can be provided by introductory textbooks to the subject. Those involved with the applications of Bayesian reasoning will find essential discussion on the validity of Bayesianism and its limits, while philosophers and others interested in pure reasoning will find new ideas on normativity and the logic of belief.