Title Declarative Logic Programming
Subtitle Theory, Systems, and Applications
Author Michael Kifer, Yanhong Annie Liu
ISBN 9781970001969
List price USD 79.95
Price outside India Available on Request
Original price
Binding Paperback
No of pages 616
Book size 191 X 235 mm
Publishing year 2018
Original publisher Morgan & Claypool Publishers (Eurospan Group)
Published in India by .
Exclusive distributors Viva Books Private Limited
Sales territory India, Sri Lanka, Bangladesh, Pakistan, Nepal, .
Status New Arrival
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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 on Warren’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.





Chapter 1. Datalog: Concepts, History, and Outlook (David Maier, K. Tuncay Tekle, Michael Rifer, David S. Warren) • Introduction • The Emergence of Datalog • Coining “Datalog” • Extensions to Datalog • Evaluation Techniques • Early Datalog and Deductive Database Systems The Decline and Resurgence of Datalog • Current Systems and Comparison • Conclusions • Acknowledgments • References

Chapter 2. An Introduction to the Stable and Well-Founded Semantics of Logic Programs (Miroslaw Truszczynski) • Introduction • Terminology, Notation, and Other Preliminaries • The Case of Horn Logic Programs • Moving Beyond Horn Programs–An Informal Introduction • The Stable Model Semantics • The Well-Founded Model Semantics • Concluding Remarks • Acknowledgments • References

Chapter 3. A Survey of Probabilistic Logic Programming (Fabrizio Riguzzi, Theresa Swift) • Introduction • Languages with the Distribution Semantics • Defining the Distribution Semantics • Other Semantics for Probabilistic Logics • Probabilistic Logic Programs and Bayesian Networks • Inferencing in Probabilistic Logic Programs • Discussion • Acknowledgments • References



Chapter 4. WAM for Everyone: A Virtual Machine for Logic Programming (David S. WArren) • Introduction • The Run-Time Environment of a Traditional Procedural Language • Deterministic Datalog • Deterministic Prolog • Nondeterministic Prolog • Last Call Optimization • Indexing • Environment Trimming • Features Required for Full Prolog • WAM Extensions for Tabling • Concluding Remarks • Acknowledgments • References

Chapter 5. Predicate Logic as a Modeling Language: The IDP System (Broes De Cat, Bart Bogaerts, Maurice Bruynooghe, GerdaJanssens, Marc Denecker) • Introduction • FO(ID, AGG, PF, T), the Formal Base Language • IDP as a Knowledge Base System • The IDP Language • Advanced Features • Under the Hood • In Practice • Related Work • Conclusion • References

Chapter 6. SolverBlox: Algebraic Modeling in Datalog (Conrado Borraz-Sánchez, Diego Klabjan, Emir Pasalic, Molham Aref) • Introduction • Datalog • LogicBlox and LogiQL • Mathematical Programming with LogiQL • The Traveling Salesman Problem (TSP) Test Case • Conclusions and Future Work • References



Chapter 7. Exploring Life: Answer Set Programming in Bioinformatics (Alessandro Dal Palù, Agostino Dovier, Andrea Formisano, Enrico Pontelli) • Introduction • Biology in a Nutshell • Answer Set Programming in a Nutshell • Phylogenetics • Haplotype Inference • RNA Secondary Structure Prediction • Protein Structure Prediction • Systems Biology • Other Logic Programming Approaches • Conclusions • Acknowledgments • References

Chapter 8. State-Space Search with Tabled Logic Programs (C. R. Ramakrishnan) • Introduction • Finite-State Model Checking • Infinite-State Model Checking • Simple Planning via Tabled Search • Discussion • Acknowledgments • References

Chapter 9. Natural Language Processing with (Tabled and Constraint) Logic Programming (Henning Christiansen, Verónica Dahl) • Introduction • Tabling, LP, and NLP • Tabled Logic Programming and Definite Clause Grammars • Using Extra Arguments for Linguistic Information • Assumption Grammars: DCGs Plus Global Memory • Constraint Handling Rules and Their Application to Language Processing • Hypothetical Reasoning with CHR and Prolog: Hyprolog • A Note on the Usefulness of Probabilistic Logic Programming for Language Processing • Conclusion • References

Chapter 10. Logic Programming Applications: What Are the Abstractions and Implementations? (Yanhong A. Liu) • Introduction • Logic Language Abstractions • Join and Database-Style Queries • Recursion and Inductive Analysis • Constraint and Combinatorial Search • Further Extensions, Applications, and Discussion • Related Literature and Future Work • Acknowledgments • References



About the Editors:

Michael Kifer is a professor with the Department of Computer Science, Stony Brook University, USA. He received his Ph.D. in Computer Science in 1984 from the Hebrew University of Jerusalem, Israel, and the M.S. degree in Mathematics in 1976 from Lomonosov Moscow State University, Russia. Since 2012, Dr. Kifer has served as the President of the Rules and Reasoning Association (RRA). His work spans the areas of knowledge representation and reasoning (KRR), logic programming, Web information systems, and databases. He published four textbooks and numerous articles in these areas as well as co-invented F-logic, HiLog, Annotated Logic, and Transaction Logic, which are among the most widely cited works in Computer Science and Semantic Web research, in particular. Twice, in 1999 and 2002, he was a recipient of the prestigious ACM-SIGMOD “Test of Time” awards for his works on F-logic and object-oriented database languages. In 2008, he received SUNY Chancellor’s Award for Excellence in Scholarship. In 2013, Dr. Kifer received another prestigious award: The 20-year “Test of Time” award from the Association for Logic Programming (ALP) for his work on Transaction Logic. In 2013, Kifer co-founded Coherent Knowledge Systems, a startup that commercializes semantic and KRR technologies.

Yanhong Annie Liu is a professor of Computer Science at Stony Brook University. She received her B.S. from Peking University, M.Eng. from Tsinghua University, and Ph.D. from Cornell University, all in Computer Science. Her primary research is in languages and algorithms, especially on systematic design and optimization, centered around incrementalization; the discrete counterpart of differentiation in calculus. Her current research focus is on languages and efficient implementations for secure distributed programming and for declarative system specifications. She has published in many prestigious venues, taught in a wide range of computer science areas, and presented over 100 conferences and invited talks worldwide. She serves on the ACM Books Editorial Board as the Area Editor for Programming Languages, and she is a member of IFIP WG 2.1 on Algorithmic Languages and Calculi. Her awards include a State University of New York Chancellor’s Award for Excellence in Scholarship and Creative Activities.

Target Audience:

This book will be useful for researchers and practitioners as well as graduate students interested in Logic Programming.

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