Roberto Barbuti

Bio: Roberto Barbuti is an academic researcher from University of Pisa. The author has contributed to research in topics: Operational semantics & Logic programming. The author has an hindex of 26, co-authored 129 publications receiving 2089 citations.

The calculus of looping sequences

Abstract: We describe the Calculus of Looping Sequences (CLS) which is suitable for modeling microbiological systems and their evolution. We present two extensions, CLS with links (LCLS) and Stochastic CLS. LCLS simplifies the description of protein interaction at a lower level of abstraction, namely at the domain level. Stochastic CLS allows us to describe quantitative aspects of the modeled systems, such as the frequency of chemical reactions. As examples of application to real biological systems, we show the simulation of the activity of the lactose operon in E.coli and the quorum sensing process in P.aeruginosa, both described with Stochastic CLS.

A general framework for semantics-based bottom-up abstract interpretation of logic programs

Abstract: The theory of abstract interpretation provides a formal framework to develop advanced dataflow analysis tools. The idea is to define a nonstandard semantics which is able to compute, in finite time, an approximated model of the program. In this paper, we define an abstract interpretation framework based on a fixpoint approach to the semantics. This leads to the definition, by means of a suitable set of operators, of an abstract fixpoint characterization of a model associated with the program. Thus, we obtain a specializable abstract framework for bottom-up abstract interpretations of definite logic programs. The specialization of the framework is shown on two examples, namely, gound-dependence analysis and depth-k analysis.

A Calculus of Looping Sequences for Modelling Microbiological Systems

Abstract: The paper presents the Calculus of Looping Sequences (CLS) suitable to describe microbiological systems and their evolution. The terms of the calculus are constructed by basic constituent elements and operators of sequencing, looping, containment and parallel composition. The looping operator allows tying up the ends of a sequence, thus creating a circular sequence which can represent a membrane. We show that a membrane calculus recently proposed can be encoded into CLS. We use our calculus to model interactions among bacteria and bacteriophage viruses, and to reason on their properties.

A transformational approach to negation in logic programming

Abstract: A transformation technique is introduced which, given the Horn-clause definition of a set of predicates p i , synthesizes the definitions of new predicate p i which can be used, under a suitable refutation procedure, to compute the finite failure set of p i . This technique exhibits some computational advantages, such as the possibility of computing nonground negative goals still preserving the capability of producing answers. The refutation procedure, named SLDN refutation, is proved sound and complete with respect to the completed program.

CHAPTER 10 – Logic Programming

Abstract: Publisher Summary This chapter focuses on logic programming, which is closely related to PROLOG. Logic programming constitutes the theoretical framework of PROLOG. This close connection led to the adoption of logic programming as the basis for the influential Japanese Fifth Generation Project. The power of logic programming stems from two reasons. First, it is an extremely simple formalism. It also relies on mathematical logic, which developed its own methods and techniques and which provides a rigorous mathematical framework. The chapter presents a self-contained introduction to the theory of logic programming. It also discusses the causal dependence between various concepts and notions and presents the notion of SLD-resolution, which is central to the subject of logic programming.

Constraint logic programming : A survey

Abstract: Constraint Logic Programming (CLP) is a merger of two declarative paradigms: constraint solving and logic programming. Although a relatively new field, CLP has progressed in several quite different directions. In particular, the early fundamental concepts have been adapted to better serve in different areas of applications. In this survey of CLP, a primary goal is to give a systematic description of the major trends in terms of common fundamental concepts. The three main parts cover the theory, implementation issues, and programming for applications.

Handbook of Constraint Programming

Abstract: Constraint programming is a powerful paradigm for solving combinatorial search problems that draws on a wide range of techniques from artificial intelligence, computer science, databases, programming languages, and operations research. Constraint programming is currently applied with success to many domains, such as scheduling, planning, vehicle routing, configuration, networks, and bioinformatics. The aim of this handbook is to capture the full breadth and depth of the constraint programming field and to be encyclopedic in its scope and coverage. While there are several excellent books on constraint programming, such books necessarily focus on the main notions and techniques and cannot cover also extensions, applications, and languages. The handbook gives a reasonably complete coverage of all these lines of work, based on constraint programming, so that a reader can have a rather precise idea of the whole field and its potential. Of course each line of work is dealt with in a survey-like style, where some details may be neglected in favor of coverage. However, the extensive bibliography of each chapter will help the interested readers to find suitable sources for the missing details. Each chapter of the handbook is intended to be a self-contained survey of a topic, and is written by one or more authors who are leading researchers in the area. The intended audience of the handbook is researchers, graduate students, higher-year undergraduates and practitioners who wish to learn about the state-of-the-art in constraint programming. No prior knowledge about the field is necessary to be able to read the chapters and gather useful knowledge. Researchers from other fields should find in this handbook an effective way to learn about constraint programming and to possibly use some of the constraint programming concepts and techniques in their work, thus providing a means for a fruitful cross-fertilization among different research areas. The handbook is organized in two parts. The first part covers the basic foundations of constraint programming, including the history, the notion of constraint propagation, basic search methods, global constraints, tractability and computational complexity, and important issues in modeling a problem as a constraint problem. The second part covers constraint languages and solver, several useful extensions to the basic framework (such as interval constraints, structured domains, and distributed CSPs), and successful application areas for constraint programming. - Covers the whole field of constraint programming - Survey-style chapters - Five chapters on applications Table of Contents Foreword (Ugo Montanari) Part I : Foundations Chapter 1. Introduction (Francesca Rossi, Peter van Beek, Toby Walsh) Chapter 2. Constraint Satisfaction: An Emerging Paradigm (Eugene C. Freuder, Alan K. Mackworth) Chapter 3. Constraint Propagation (Christian Bessiere) Chapter 4. Backtracking Search Algorithms (Peter van Beek) Chapter 5. Local Search Methods (Holger H. Hoos, Edward Tsang) Chapter 6. Global Constraints (Willem-Jan van Hoeve, Irit Katriel) Chapter 7. Tractable Structures for CSPs (Rina Dechter) Chapter 8. The Complexity of Constraint Languages (David Cohen, Peter Jeavons) Chapter 9. Soft Constraints (Pedro Meseguer, Francesca Rossi, Thomas Schiex) Chapter 10. Symmetry in Constraint Programming (Ian P. Gent, Karen E. Petrie, Jean-Francois Puget) Chapter 11. Modelling (Barbara M. Smith) Part II : Extensions, Languages, and Applications Chapter 12. Constraint Logic Programming (Kim Marriott, Peter J. Stuckey, Mark Wallace) Chapter 13. Constraints in Procedural and Concurrent Languages (Thom Fruehwirth, Laurent Michel, Christian Schulte) Chapter 14. Finite Domain Constraint Programming Systems (Christian Schulte, Mats Carlsson) Chapter 15. Operations Research Methods in Constraint Programming (John Hooker) Chapter 16. Continuous and Interval Constraints(Frederic Benhamou, Laurent Granvilliers) Chapter 17. Constraints over Structured Domains (Carmen Gervet) Chapter 18. Randomness and Structure (Carla Gomes, Toby Walsh) Chapter 19. Temporal CSPs (Manolis Koubarakis) Chapter 20. Distributed Constraint Programming (Boi Faltings) Chapter 21. Uncertainty and Change (Kenneth N. Brown, Ian Miguel) Chapter 22. Constraint-Based Scheduling and Planning (Philippe Baptiste, Philippe Laborie, Claude Le Pape, Wim Nuijten) Chapter 23. Vehicle Routing (Philip Kilby, Paul Shaw) Chapter 24. Configuration (Ulrich Junker) Chapter 25. Constraint Applications in Networks (Helmut Simonis) Chapter 26. Bioinformatics and Constraints (Rolf Backofen, David Gilbert)

Towards a theory of declarative knowledge

Abstract: We identify a useful class of logic programs with negation, called stratified programs, that disallow certain combinations of recursion and negation. Programs in this class have a simple declarative and procedural meaning based, respectively, on model theory and a back-chaining interpreter. The standard model of a stratified program, which gives the program a declarative meaning and is independent of the stratification, is characterized in two ways. One is based on a fixed point theory of nonmonotonic operators and the other on an abstract declarative characterization. The back-chaining interpreter also determines the standard model. Finally, we prove the consistency of Clark's completion for stratified programs and attempt to clarify the sources of some previously reported difficulties with negation in logic programming.

Abstract interpretation and application to logic programs

Abstract: interpretation is a theory of semantics approximation that is used for the construction of semantic-based program analysis algorithms (sometimes called “data flow analysis”), the comparison of formal semantics (e.g., construction of a denotational semantics from an operational one), design of proof methods, etc. Automatic program analysers are used for determining statistically conservative approximations of dynamic properties of programs. Such properties of the run-time behavior of programs are useful for debugging (e.g., type inference), code optimization (e.g., compile-time garbage collection, useless occur-check elimination), program transformation (e.g., partial evaluation, parallelization), and even program correctness proofs (e.g., termination proof). After a few simple introductory examples, we recall the classical framework for abstract interpretation of programs. Starting from a ground operational semantics formalized as a transition system, classes of program properties are first encapsulated in collecting semantics expressed as fixpoints on partial orders representing concrete program properties. We consider invariance properties characterizing descendants of the initial states (corresponding to top/down or forward analyses), ascendant states of the final states (corresponding to bottom/up or backward analyses) as well as a combination of the two. Then we choose specific approximate abstract properties to be gathered about program behaviors and express them as elements of a poset of abstract properties. The correspondence between concrete and abstract properties is established by a concretization and abstraction function that is a Galois connection formalizing the loss of information. We can then constructively derive the abstract program properties from the collecting semantics by a formal computation leading to a fixpoint expression in terms of abstract operators on the domain of abstract properties. The design of the abstract interpreter then involves the choice of a chaotic iteration strategy to solve this abstract fixpoint equation. We insist on the compositional design of this abstract interpreter, which is formalized by a series of propositions for designing Galois connections (such as Moore families, decomposition by partitioning, reduced product, down-set completion, etc.). Then we recall the convergence acceleration methods using widening and narrowing allowing for the use of very expressive infinite domains of abstract properties. We show that this classical formal framework can be applied in extenso to logic programs. For simplicity, we use a variant of SLD-resolution as the ground operational semantics. The first example is groundness analysis, which is a variant of Mellish mode analysis. It is extended to a combination of top/down and bottom/up analyses. The second example is the derivation of constraints among argument sizes, which involves an infinite abstract domain requiring the use of convergence accelaration methods. We end up with a short thematic guide to the literature on abstract interpretation of logic programs.