Objects for Concurrent Constraint Programming

Henz, Martin

doi:10.1007/978-1-4615-5477-6

Cited by 10 publications

(7 citation statements)

References 33 publications

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“…Mozart was chosen because it has performance competitive with commercial products [11], despite of being freeware and open-source. Several books have been written about Mozart/Oz ( [20], [17], [3], and [6]), and the programming system is available for free at [11].…”

Section: Ccp-based Approach To Path Planningmentioning

confidence: 99%

Concurrent constraint programming-based path planning for uninhabited air vehicles

Gualandi

Tranchero

2004

Unattended/Unmanned Ground, Ocean, and Air Sensor Technologies and Applications VI

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This paper describes a study performed with the objective of investigating Concurrent Constraint Programming (CCP) as the main tool for the design and the implementation of a software path planner. The main features of the path planning are introduced along with some modeling and implementation issues. The CCP approach is motivated by the facility of translating complex models with domain specific features into efficient implementations. In order to use CCP, the path planning is formalized as a constraint satisfaction problem by defining variables, domains, and constraints. The proposed solution is as general as possible, and it is widely applicable in several contexts. A demo application has been implemented, and it is described in this paper. The application comes with a graphical interface that allow the user to define the mission constraints. The output of the application is a path plan, i.e. a list of waypoints that can also be displayed on a geographical map. The estimated path length, fuel consumption, and path risk are given as well.

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Section: Ccp-based Approach To Path Planningmentioning

confidence: 99%

Concurrent constraint programming-based path planning for uninhabited air vehicles

Gualandi

Tranchero

2004

Unattended/Unmanned Ground, Ocean, and Air Sensor Technologies and Applications VI

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“…This is sufficient for sequential object-oriented programming (Smolka, 1995b;Henz, 1997b;Henz, 1997a). Another, equivalent way to add state is by means of ports, which are explained in Section 5.…”

Section: The Kernel Languagementioning

confidence: 99%

“…Oz has a full-featured concurrent object system which is completely defined in terms of cells (Henz, 1997b;Henz, 1997a). The object system includes multiple inheritance, fine-grained method access control, and first-class messages.…”

Section: Cells (Mutable References)mentioning

confidence: 99%

“…With ports it is natural to define active objects that send messages to each other. From a theoretical point of view, these two programming styles have the same expressiveness, since cells and ports can be defined in terms of each other without changing time or space complexity (Henz, 1997a;Lauer & Needham, 1978). They differ in practice, since depending on the application one style might be more convenient than the other.…”

Section: Ports (Communication Channels)mentioning

confidence: 99%

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Logic programming in the context of multiparadigm programming: the Oz experience

Roy¹,

Brand²,

Duchier³

et al. 2003

Theory and Practice of Logic Programming

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Oz is a multiparadigm language that supports logic programming as one of its major paradigms. A multiparadigm language is designed to support different programming paradigms (logic, functional, constraint, object-oriented, sequential, concurrent, etc.) with equal ease. This article has two goals: to give a tutorial of logic programming in Oz and to show how logic programming fits naturally into the wider context of multiparadigm programming. Our experience shows that there are two classes of problems, which we call algorithmic and search problems, for which logic programming can help formulate practical solutions. Algorithmic problems have known efficient algorithms. Search problems do not have known efficient algorithms but can be solved with search. The Oz support for logic programming targets these two problem classes specifically, using the concepts needed for each. This is in contrast to the Prolog approach, which targets both classes with one set of concepts, which results in less than optimal support for each class. We give examples that can be run interactively on the Mozart system, which implements Oz. To explain the essential difference between algorithmic and search programs, we define the Oz execution model. This model subsumes both concurrent logic programming (committed-choice-style) and search-based logic programming (Prolog-style). Furthermore, as consequences of its multiparadigm nature, the model supports new abilities such as first-class top levels, deep * This article is a much-extended version of the tutorial talk "Logic Programming in Oz withMozart" given at the International Conference on Logic Programming, Las Cruces, New Mexico, Nov. 1999. Some knowledge of traditional logic programming (with Prolog or concurrent logic languages) is assumed. guards, active objects, and sophisticated control of the search process. Instead of Horn clause syntax, Oz has a simple, fully compositional, higher-order syntax that accommodates the abilities of the language. We give a brief history of Oz that traces the development of its main ideas and we summarize the lessons learned from this work. Finally, we give many entry points into the Oz literature.

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“…Unification is extended to handle constrained logic variables according to their logical meaning. For instance, the expression let val x = fdvar [1,2] val y = fdvar[0,2] in x == y end is equivalent to the expression 2.…”

Section: Finite Domain Constraintsmentioning

confidence: 99%

Concurrent constraint programming based on functional programming

Smolka

1998

Programming Languages and Systems

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Objects for Concurrent Constraint Programming

Cited by 10 publications

References 33 publications

Concurrent constraint programming-based path planning for uninhabited air vehicles

Concurrent constraint programming-based path planning for uninhabited air vehicles

Logic programming in the context of multiparadigm programming: the Oz experience

Concurrent constraint programming based on functional programming

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