An operational semantics for probabilistic concurrent constraint programming

The paper investigates a probabilistic version of the concurrent constraint programming paradigm (CCP). The aim is to introduce the possibility to formulate so called "randomised algorithms" within the CCP framework. Differently from common approaches in (imperative) high level programming...

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Published in:Proceedings - International Conference on Computer Languages pp. 174 - 183
Main Authors: Di Pierro, A., Wiklicky, H.
Format: Conference Proceeding Journal Article
Language:English
Published: IEEE 1998
Subjects:
Application software
Computational geometry
Computational modeling
Computer languages
Computer science
Logic testing
Mathematics
Monte Carlo methods
Simulated annealing
Stochastic processes
ISBN:9780818684548, 0818684542
ISSN:1074-8970
Online Access:Get full text
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Abstract The paper investigates a probabilistic version of the concurrent constraint programming paradigm (CCP). The aim is to introduce the possibility to formulate so called "randomised algorithms" within the CCP framework. Differently from common approaches in (imperative) high level programming languages, which rely on some kind of random() function, we introduce randomness in the very definition of the language by means of a probabilistic choice construct. This allows a program to make stochastic moves during its execution. We call the resulting language probabilistic concurrent constraint programming (PCCP). We present an operational semantics for PCCP by means of a probabilistic transition system such that the execution of a PCCP program may be seen as a stochastic process, i.e. as a random walk on the transition graph. The transition probabilities are given explicitly. This semantics captures a notion of observables which combines results of computations and the probability of those results being computed. This embedding of randomness within the semantics of a well structured programming paradigm, like CCP, also aims at providing a sound framework for formalising and reasoning about randomised algorithms and programs. Additionally, we give some examples of PCCP programs and we show how well known randomised algorithms can be implemented very naturally in the new language.
AbstractList The paper investigates a probabilistic version of the concurrent constraint programming paradigm (CCP). The aim is to introduce the possibility to formulate so called "randomised algorithms" within the CCP framework. Differently from common approaches in (imperative) high level programming languages, which rely on some kind of random() function, we introduce randomness in the very definition of the language by means of a probabilistic choice construct. This allows a program to make stochastic moves during its execution. We call the resulting language probabilistic concurrent constraint programming (PCCP). We present an operational semantics for PCCP by means of a probabilistic transition system such that the execution of a PCCP program may be seen as a stochastic process, i.e. as a random walk on the transition graph. The transition probabilities are given explicitly. This semantics captures a notion of observables which combines results of computations and the probability of those results being computed. This embedding of randomness within the semantics of a well structured programming paradigm, like CCP, also aims at providing a sound framework for formalising and reasoning about randomised algorithms and programs. Additionally, we give some examples of PCCP programs and we show how well known randomised algorithms can be implemented very naturally in the new language.
This paper investigates a probabilistic version of the concurrent constraint programming paradigm (CCP). The aim is to introduce the possibility to formulate so called `randomized algorithms' within the CCP framework. Differently from common approaches in (imperative) high-level programming languages, which rely on some kind of random() function, we introduce randomness in the very definition of the language by means of a probabilistic choice construct. This allows a program to make stochastic moves during its execution. We call the resulting language Probabilistic Concurrent Constraint Programming (PCCP). We present an operational semantics for PCCP by means of a probabilistic transition system such that the execution of a PCCP program may be seen as a stochastic process, i.e. as a random walk on the transition graph. The transition probabilities are given explicitly. This semantics captures a notion of observables which combines results of computations and the probability of those results being computed. This embedding of randomness within the semantics of a well structured programming paradigm, like CCP, also aims at providing a sound framework for formalizing and reasoning about randomized algorithms and programs. Additionally, we give some examples of PCCP programs and we show how well-known randomized algorithms can be implemented very naturally in the new language.
Author Di Pierro, A.
Wiklicky, H.
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Snippet The paper investigates a probabilistic version of the concurrent constraint programming paradigm (CCP). The aim is to introduce the possibility to formulate so...
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SubjectTerms Application software
Computational geometry
Computational modeling
Computer languages
Computer science
Logic testing
Mathematics
Monte Carlo methods
Simulated annealing
Stochastic processes
Title An operational semantics for probabilistic concurrent constraint programming
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