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Thom Frühwirth - One of the best experts on this subject based on the ideXlab platform.

  • Concurrent Constraint Logic Programming
    Cognitive Technologies, 2020
    Co-Authors: Thom Frühwirth, Slim Abdennadher
    Abstract:

    At the end of the 1980’s, concurrent Constraint Logic Programming (CCLP) integrated ideas from concurrent LP [52] and CLP (Fig. 6.1).

  • PADL - Spatio-temporal Annotated Constraint Logic Programming
    Practical Aspects of Declarative Languages, 2001
    Co-Authors: Alessandra Raffaeta, Thom Frühwirth
    Abstract:

    We extend Temporal Annotated Constraint Logic Programming (TACLP) in order to obtain a framework where both temporal and spatial information can be dealt with and reasoned about. This results in a conceptually simple, uniform setting, called STACLP (Spatio-Temporal Annotated Constraint Logic Programming), where temporal and spatial data are represented by means of annotations that label atomic first-order formulae. The expressiveness and conciseness of the approach are illustrated by means of some examples: Definite, periodic and indefinite spatio-temporal information involving time-varying objects and properties can be handled in a natural way.

  • spatio temporal annotated Constraint Logic Programming
    Practical Aspects of Declarative Languages, 2001
    Co-Authors: Alessandra Raffaeta, Thom Frühwirth
    Abstract:

    We extend Temporal Annotated Constraint Logic Programming (TACLP) in order to obtain a framework where both temporal and spatial information can be dealt with and reasoned about. This results in a conceptually simple, uniform setting, called STACLP (Spatio-Temporal Annotated Constraint Logic Programming), where temporal and spatial data are represented by means of annotations that label atomic first-order formulae. The expressiveness and conciseness of the approach are illustrated by means of some examples: Definite, periodic and indefinite spatio-temporal information involving time-varying objects and properties can be handled in a natural way.

  • Semantics for temporal annotated Constraint Logic Programming
    Applied Logic Series, 2000
    Co-Authors: Alessandra Raffaeta, Thom Frühwirth
    Abstract:

    We investigate the semantics of a considerable subset of Temporal Annotated Constraint Logic Programming (TACLP), a class of languages that allows us to reason about qualitative and quantitative, definite and indefinite temporal information using time points and time periods as labels for atoms.

  • temporal annotated Constraint Logic Programming
    Journal of Symbolic Computation, 1996
    Co-Authors: Thom Frühwirth
    Abstract:

    Abstract We introduce a family of Logics and associated Programming languages for representing and reasoning about time. The family is conceptually simple while allowing for different models of time. Formulae can be labelled with temporal information using annotations. In this way we avoid the proliferation of variables and quantifiers as encountered in first order approaches. Unlike temporal Logic, both qualitative and quantitative (metric) temporal reasoning about definite and indefinite information with time points (instants) and time periods (temporal intervals) in different models of time are supported. Our temporal annotated Logic can be made an instance of annotated Constraint Logic, which is also presented in this paper. Given a Logic in this framework, there is a systematic way to make a clausal fragment executable as a Constraint Logic program. We show this for the generic case and for the specific case of temporal annotated Logic. In both cases we give an interpreter and a compiler that can be implemented in standard Constraint Logic Programming languages.

Francesca Rossi - One of the best experts on this subject based on the ideXlab platform.

  • Constraint Logic Programming
    A 25-year perspective on logic programming, 2010
    Co-Authors: Marco Gavanelli, Francesca Rossi
    Abstract:

    Constraint Logic Programming (CLP) is one of the most successful branches of Logic Programming; it attracts the interest of theoreticians and practitioners, and it is currently used in many commercial applications. Since the original proposal, it has developed enormously: many languages and systems are now available either as open source programs or as commercial systems. Also, CLP has been one of the technologies able to recruit researchers from other communities to the declarative Programming cause. Current CLP engines include technologies and results developed in other communities, which themselves discovered Logic as an invaluable tool to model and solve real-life problems.

  • semiring based Constraint Logic Programming syntax and semantics
    ACM Transactions on Programming Languages and Systems, 2001
    Co-Authors: Stefano Bistarelli, Ugo Montanari, Francesca Rossi
    Abstract:

    We extend the Constraint Logic Programming (CLP) formalism in order to handle semiring-based Constraints. This allows us to perform in the same language both Constraint solving and optimization. In fact, Constraints based on semirings are able to model both classical Constraint solving and more sophisticated features like uncertainty, probability, fuzziness, and optimization. We then provide this class of languages with three equivalent semantics: model-theoretic, fix-point, and proof-theoretic, in the style of classical CLP programs.

  • Constraint Logic Programming a survey on research and applications
    Lecture Notes in Computer Science, 2000
    Co-Authors: Francesca Rossi
    Abstract:

    Constraint Logic Programming (CLP) is a multidisciplinary research area which can be located between Artificial Intelligence, Operation Research, and Programming Languages, and has to do with modeling, solving, and Programming real-life problems which can be described as a set of statements (the Constraints) which describe some relationship between the problem's variables. This survey paper gives a brief introduction to C(L)P, presents a (necessarily partial) state of the art in CLP research and applications, points out some promising directions for future applications, and discusses how to cope with current research challenges.

  • semiring based Constraint Logic Programming
    International Joint Conference on Artificial Intelligence, 1997
    Co-Authors: Stefano Bistarelli, Ugo Montanari, Francesca Rossi
    Abstract:

    We extend the Constraint Logic Programming (CLP) formalism in order to handle semiringbased Constraint systems. This allows us to perform in the same language both Constraint solving and optimization. In fact, Constraint systems based on semirings are able to model both classical Constraint solving and more sophisticated features like uncertainty, probability, fuzzyness, and optimization. We then provide this class of languages with three equivalent semantics: model-theoretic, fixpoint, and proof-theoretic, in the style of CLP programs.

Alessandra Raffaeta - One of the best experts on this subject based on the ideXlab platform.

  • PADL - Spatio-temporal Annotated Constraint Logic Programming
    Practical Aspects of Declarative Languages, 2001
    Co-Authors: Alessandra Raffaeta, Thom Frühwirth
    Abstract:

    We extend Temporal Annotated Constraint Logic Programming (TACLP) in order to obtain a framework where both temporal and spatial information can be dealt with and reasoned about. This results in a conceptually simple, uniform setting, called STACLP (Spatio-Temporal Annotated Constraint Logic Programming), where temporal and spatial data are represented by means of annotations that label atomic first-order formulae. The expressiveness and conciseness of the approach are illustrated by means of some examples: Definite, periodic and indefinite spatio-temporal information involving time-varying objects and properties can be handled in a natural way.

  • spatio temporal annotated Constraint Logic Programming
    Practical Aspects of Declarative Languages, 2001
    Co-Authors: Alessandra Raffaeta, Thom Frühwirth
    Abstract:

    We extend Temporal Annotated Constraint Logic Programming (TACLP) in order to obtain a framework where both temporal and spatial information can be dealt with and reasoned about. This results in a conceptually simple, uniform setting, called STACLP (Spatio-Temporal Annotated Constraint Logic Programming), where temporal and spatial data are represented by means of annotations that label atomic first-order formulae. The expressiveness and conciseness of the approach are illustrated by means of some examples: Definite, periodic and indefinite spatio-temporal information involving time-varying objects and properties can be handled in a natural way.

  • Semantics for temporal annotated Constraint Logic Programming
    Applied Logic Series, 2000
    Co-Authors: Alessandra Raffaeta, Thom Frühwirth
    Abstract:

    We investigate the semantics of a considerable subset of Temporal Annotated Constraint Logic Programming (TACLP), a class of languages that allows us to reason about qualitative and quantitative, definite and indefinite temporal information using time points and time periods as labels for atoms.

  • Temporal annotated Constraint Logic Programming with multiple theories
    Proceedings. Tenth International Workshop on Database and Expert Systems Applications. DEXA 99, 1999
    Co-Authors: P. Mancarella, Alessandra Raffaeta, F. Turini
    Abstract:

    The paper aims at building a framework that provides basic operators, with a clear semantics, for combining different knowledge bases and allowing the representation and the handling of temporal information. Our approach stems from two separate lines of research: the general studies on meta-level operators on Logic programs introduced by Brogi et al. (1993, 1994) and temporal annotated Constraint Logic Programming (TACLP) defined by Fruhwirth (1996). We propose a language MuTACLP that integrates the above mentioned approaches. Atoms are annotated with temporal information and such annotations are managed via a Constraint theory as in TACLP. Mechanisms for structuring programs and combining separate knowledge bases are provided through meta-level operators. A top-down semantics of MuTACLP is given by exploiting meta-Logic.

David S Warren - One of the best experts on this subject based on the ideXlab platform.

  • Computational Logic - A System for Tabled Constraint Logic Programming
    Lecture Notes in Computer Science, 2000
    Co-Authors: David S Warren
    Abstract:

    As extensions to traditional Logic Programming, both tabling and Constraint Logic Programming (CLP) have proven powerful tools in many areas. They make Logic Programming more efficient and more declarative. However, combining the techniques of tabling and Constraint solving is still a relatively new research area. In this paper, we show how to build a Tabled Constraint Logic Programming (TCLP) system based on XSB -- a tabled Logic Programming system. We first discuss how to extend XSB with the fundamental mechanism of Constraint solving, basically the introduction of attributed variables to XSB, and then present a general framework for building a TCLP system. An interface among the XSB tabling engine, the corresponding Constraint solver, and the user's program is designed to fully utilize the power of tabling in TCLP programs.

  • a system for tabled Constraint Logic Programming
    Lecture Notes in Computer Science, 2000
    Co-Authors: David S Warren
    Abstract:

    As extensions to traditional Logic Programming, both tabling and Constraint Logic Programming (CLP) have proven powerful tools in many areas. They make Logic Programming more efficient and more declarative. However, combining the techniques of tabling and Constraint solving is still a relatively new research area. In this paper, we show how to build a Tabled Constraint Logic Programming (TCLP) system based on XSB -- a tabled Logic Programming system. We first discuss how to extend XSB with the fundamental mechanism of Constraint solving, basically the introduction of attributed variables to XSB, and then present a general framework for building a TCLP system. An interface among the XSB tabling engine, the corresponding Constraint solver, and the user's program is designed to fully utilize the power of tabling in TCLP programs.

Richard S Zemel - One of the best experts on this subject based on the ideXlab platform.

  • neural guided Constraint Logic Programming for program synthesis
    Neural Information Processing Systems, 2018
    Co-Authors: Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William E Byrd, Matthew Might, Raquel Urtasun, Richard S Zemel
    Abstract:

    Synthesizing programs using example input/outputs is a classic problem in artificial intelligence. We present a method for solving Programming By Example problems by using a neural model to guide the search of a Constraint Logic Programming system called miniKanren. Internally, miniKanren represents a PBE problem as recursive Constraints imposed by the provided examples. We present a Recurrent Neural Network model and a Gated Graph Neural Network model, both of which use these Constraints as input to score candidate programs. We further present a transparent version of miniKanren that can be driven by an external agent, suitable for use by other researchers. We show that our neural-guided approach using Constraints can synthesize problems faster in many cases, and has the potential to generalize to larger problems.

  • neural guided Constraint Logic Programming for program synthesis
    arXiv: Learning, 2018
    Co-Authors: Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William E Byrd, Matthew Might, Raquel Urtasun, Richard S Zemel
    Abstract:

    Synthesizing programs using example input/outputs is a classic problem in artificial intelligence. We present a method for solving Programming By Example (PBE) problems by using a neural model to guide the search of a Constraint Logic Programming system called miniKanren. Crucially, the neural model uses miniKanren's internal representation as input; miniKanren represents a PBE problem as recursive Constraints imposed by the provided examples. We explore Recurrent Neural Network and Graph Neural Network models. We contribute a modified miniKanren, drivable by an external agent, available at this https URL We show that our neural-guided approach using Constraints can synthesize programs faster in many cases, and importantly, can generalize to larger problems.