26,771 research outputs found
Parallelism in declarative languages
Imperative programming languages were initially built for uniprocessor systems that evolved out of the Von Neumann machine model. This model of storage oriented computation blocks parallelism and increases the cost of parallel program development and porting. Declarative languages based on mathematical models of computation, seem more suitable for the development of parallel programs. In the first part of this thesis we examine different language families under the declarative paradigm: functional, logic, and constraint languages. Functional languages are based on the abstract model of functions and (lamda)-calculus. They were initially developed for symbolic computation, but today they are commonly used in numerical analysis and many other application areas. Pure lisp is a widely known member of this class. Logic languages are based on first order predicate calculus. Although they were initially developed for theorem proving, fifth generation operating systems are written in them. Most logic languages are descendants or distant relatives of Prolog. Constraint languages are related to logic languages. In a constraint language you define a program object by placing constraints on its structure and its behavior. They were initially used in graphics applications, but today researchers work on using them in parallel computation. Here we will compare and contrast the language classes above, locate advantages and deficiencies, and explain different choices made by language implementors. In the second part of thesis we describe a front end for the CONSUL, a prototype constraint language for programming multiprocessors. The most important features of the front end are compact representation of constraints, type definitions, functional use of relations, and the ability to split programs into multiple files
Certainty Closure: Reliable Constraint Reasoning with Incomplete or Erroneous Data
Constraint Programming (CP) has proved an effective paradigm to model and
solve difficult combinatorial satisfaction and optimisation problems from
disparate domains. Many such problems arising from the commercial world are
permeated by data uncertainty. Existing CP approaches that accommodate
uncertainty are less suited to uncertainty arising due to incomplete and
erroneous data, because they do not build reliable models and solutions
guaranteed to address the user's genuine problem as she perceives it. Other
fields such as reliable computation offer combinations of models and associated
methods to handle these types of uncertain data, but lack an expressive
framework characterising the resolution methodology independently of the model.
We present a unifying framework that extends the CP formalism in both model
and solutions, to tackle ill-defined combinatorial problems with incomplete or
erroneous data. The certainty closure framework brings together modelling and
solving methodologies from different fields into the CP paradigm to provide
reliable and efficient approches for uncertain constraint problems. We
demonstrate the applicability of the framework on a case study in network
diagnosis. We define resolution forms that give generic templates, and their
associated operational semantics, to derive practical solution methods for
reliable solutions.Comment: Revised versio
The CIAO multiparadigm compiler and system: A progress report
Abstract is not available
CASP Solutions for Planning in Hybrid Domains
CASP is an extension of ASP that allows for numerical constraints to be added
in the rules. PDDL+ is an extension of the PDDL standard language of automated
planning for modeling mixed discrete-continuous dynamics.
In this paper, we present CASP solutions for dealing with PDDL+ problems,
i.e., encoding from PDDL+ to CASP, and extensions to the algorithm of the EZCSP
CASP solver in order to solve CASP programs arising from PDDL+ domains. An
experimental analysis, performed on well-known linear and non-linear variants
of PDDL+ domains, involving various configurations of the EZCSP solver, other
CASP solvers, and PDDL+ planners, shows the viability of our solution.Comment: Under consideration in Theory and Practice of Logic Programming
(TPLP
- …