We introduce a new nameless representation of lambda terms inspired by ordered logic. At a lambda abstraction, number and relative position of all occurrences of the bound variable are stored, and application carries ...
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In this paper, we describe the research lines in logicprogramming, carried out in Cosenza over a period of more than 20 years, which have recently produced promising industrial exploitation follow-ups. The research l...
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In this paper, we describe the research lines in logicprogramming, carried out in Cosenza over a period of more than 20 years, which have recently produced promising industrial exploitation follow-ups. The research lines have changed over the time but they have kept the initial interest on combining logicprogramming with databases techniques, that has been continuously renewed to cope with new challenges, in our attempt to use theory to solve practical problems.
Metabolomics is increasingly becoming an important field. The fundamental task in this area is to measure and interpret complex time and condition dependent parameters such as the activity or flux of metabolites in ce...
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Metabolomics is increasingly becoming an important field. The fundamental task in this area is to measure and interpret complex time and condition dependent parameters such as the activity or flux of metabolites in cells, their concentration, tissues elements and other biosamples. The careful study of all these elements has led to important insights in the functioning of metabolism. Recently, however, there is a growing interest towards an integrated approach to studying biological systems. This is the main goal in Systems Biology where a combined investigation of several components of a biological system is thought to produce a thorough understanding of such systems. Biological circuits are complex to model and simulate and many efforts are being made to develop models that can handle their intrinsic complexity. A significant part of biological networks still remains unknown even though recent technological developments allow simultaneous acquisition of many metabolite measurements. Metabolic networks are not only structurally complex but behave also in a stochastic fashion. Therefore, it is necessary to express structure and handle uncertainty to construct complete dynamics of these networks. In this paper we describe how stochastic modeling and simulation can be performed in a symbolic-statistical machine learning (ML) framework. We show that symbolic ML deal with structural and relational complexity while statistical ML provides principled approaches to uncertainty modeling. Learning is used to analyze traces of biochemical reactions and model the dynamicity through parameter learning, while inference is used to produce stochastic simulation of the network. (C) 2011 Elsevier B. V. All rights reserved.
The model checking of higher-order recursion schemes (higher-order model checking for short) has been actively studied in the last decade, and has seen significant progress in both theory and practice. From a practica...
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The model checking of higher-order recursion schemes (higher-order model checking for short) has been actively studied in the last decade, and has seen significant progress in both theory and practice. From a practical perspective, higher-order model checking provides a foundation for software model checkers for functional programming languages such as ML and Haskell. This short article aims to provide an overview of the recent progress in higher-order model checking and discuss future directions.
programming languages are often classified according to their paradigms, e.g. imperative, functional, logic, constraint-based, object-oriented, or aspect-oriented. A paradigm characterizes the style, concepts, and met...
ISBN:
(数字)9783642173301
ISBN:
(纸本)9783642173295
programming languages are often classified according to their paradigms, e.g. imperative, functional, logic, constraint-based, object-oriented, or aspect-oriented. A paradigm characterizes the style, concepts, and methods of the language for describing situations and processes and for solving problems, and each paradigm serves best for programming in particular application areas. Real-world problems, however, are often best implemented by a combination of concepts from different paradigms, because they comprise aspects from several realms, and this combination is more comfortably realized using multiparadigm programming languages. This book deals with the theory and practice of multiparadigm constraint programming languages. The author first elaborates on programming paradigms and languages, constraints, and the merging of programming concepts which yields multiparadigm (constraint) programming languages. In the second part the author inspects two concrete approaches on multiparadigm constraint programming the concurrent constraint functional language CCFL, which combines the functional and the constraint-based paradigms and allows the description of concurrent processes; and a general framework for multiparadigm constraint programming and its implementation, *** book is appropriate for researchers and graduate students in the areas of programming and artificial intelligence.
Take advantage of the growing trend in functional programming. C# is the number-one language used by .NET developers and one of the most popular programming languages in the world. It has many built-in functional prog...
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ISBN:
(数字)9780470970287
ISBN:
(纸本)9780470744581
Take advantage of the growing trend in functional programming. C# is the number-one language used by .NET developers and one of the most popular programming languages in the world. It has many built-in functional programming features, but most are complex and little understood. With the shift to functional programming increasing at a rapid pace, you need to know how to leverage your existing skills to take advantage of this trend. Functional programming in C# leads you along a path that begins with the historic value of functional ideas. Inside, C# MVP and functional programming expert Oliver Sturm explains the details of relevant language features in C# and describes theory and practice of using functional techniques in C#, including currying, partial application, composition, memoization, and monads. Next, he provides practical and versatile examples, which combine approaches to solve problems in several different areas, including complex scenarios like concurrency and high-performance calculation frameworks as well as simpler use cases like Web Services and business logic implementation. Shows how C# developers can leverage their existing skills to take advantage of functional programming Uses very little math theory and instead focuses on providing solutions to real development problems with functional programming methods, unlike traditional functional programming titles Includes examples ranging from simple cases to more complex scenarios Let Functional programming in C# show you how to get in front of the shift toward functional programming.
The logicprogramming (LP) community, through the Association for logicprogramming (ALP) and its Executive Committee, decided to introduce for 2010 important changes in the way the main yearly results in LP and relat...
The logicprogramming (LP) community, through the Association for logicprogramming (ALP) and its Executive Committee, decided to introduce for 2010 important changes in the way the main yearly results in LP and related areas are published. Whereas such results have appeared to date in standalone volumes of proceedings of the yearly International Conferences on logicprogramming (ICLP), and this method—fully in the tradition of Computer Science (CS)—has served the community well, it was felt that an effort needed to be made to achieve a higher level of compatibility with the publishing mechanisms of other fields outside CS.
Answer set programming (ASP) is a method for solving hard problems using computational logic. We describe a problem as a set of formulas of a declarative logical language in such way that the solutions correspond to t...
Answer set programming (ASP) is a method for solving hard problems using computational logic. We describe a problem as a set of formulas of a declarative logical language in such way that the solutions correspond to the models (answer sets) of the set and then use a general-purpose inference engine to find the answer sets.
In this work we define an ASP language, cardinality constraint programs (CCP). The language extends normal logic programs by adding cardinality and conditional literals as well as choice rules. These extensions allow us to represent many if not most NP-complete problems in a concise and intuitive way. The language is defined in two phases where we first introduce a simple basic language and then define the constructs of the full language in terms of translations to the basic language.
The language has a declarative formal semantics that is based on the stable model semantics of normal logic programs. The semantics of a program with variables is defined via its ground instantiation. In addition of using the Herbrand instantiation a program can be instantiated with respect to some other universe, which makes it possible to have a direct support for interpreted functions in the semantics.
The semantics is undecidable in the general case. We identify a syntactic subclass of CCPs, namely omega-restricted programs, that are decidable even when function symbols are allowed. The stable models of such programs are created by a finite relevant instantiation that we can always compute. We analyze the computational complexity of omega-restricted programs and show that deciding whether a program has a stable model is 2-NEXP-complete. We identify further subclasses of programs that are NP- and NEXP-complete in the same sense. We also present an algorithm for instantiating omega-restricted programs.
We discuss programming methodology and show how we can create uniform CCP encodings for different problems using the generate-and-test methodology. We examine ho
In this paper we address an issue that has been brought to the attention of the database community with the advent of the Semantic Web, i.e., the issue of how ontologies (and semantics conveyed by them) can help solvi...
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In this paper we address an issue that has been brought to the attention of the database community with the advent of the Semantic Web, i.e., the issue of how ontologies (and semantics conveyed by them) can help solving typical database problems, through a better understanding of Knowledge Representation (KR) aspects related to databases. In particular, we investigate this issue from the 1LP perspective by considering two database problems, (i) the definition of views and (ii) the definition of constraints, for a database whose schema is represented also by means of an ontology. Both can be reformulated as I LP problems and can benefit from the expressive and deductive power of the KR framework DL+LOG(V). We illustrate the application scenarios by means of examples.
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