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

Abstract

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.

Grammatical error simulation for computer-assisted language learning

“…Many real-world problems can be formalized as constraint satisfaction problems, so several high-level languages are available in the field of constraint programming, such as OPL [33], Oz [34], Eclipse [3], Zinc [18], and Minizinc [21]. However, these methods do not allow probabilistic inference.…”

Functional and Constraint Logic Programming

“…However, referring to Java types is done rather artificially by means of string literals which cannot be checked by a compiler, and free objects and accessing their fields are not considered. As a non-Prolog-based example, Oz is a constraint language that offers OO features, but does not seem to support constraints involving logic objects [15]. Despite their integration, the mentioned programming languages follow a declarative style, which might not be as accessible for developers who are used to imperative languages.…”

Communicating sequential processes for distributed constraint satisfaction

“…Oz is a multi-paradigm language [10] that offers powerful constraint solving facilities and has been used in a number of projects. OZ (and Mozart) supports the logic programming paradigm along with concurrent object oriented functional programming and, to the opinion of the authors, presents a new powerful programming paradigm, different than that CSPCONS SPCONS follows, i.e.…”