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

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. 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). Instead of Horn clause syntax, Oz has a simple, fully compositional, higher-order syntax that accommodates the abilities of the language. We conclude with lessons learned from this work, a brief history of Oz, and many entry points into the Oz literature.Comment: 48 pages, to appear in the journal "Theory and Practice of Logic Programming

TOR: modular search with hookable disjunction

Horn Clause Programs have a natural exhaustive depth-first procedural semantics. However, for many programs this semantics is ineffective. In order to compute useful solutions, one needs the ability to modify the search method that explores the alternative execution branches. Tor, a well-defined hook into Prolog disjunction, provides this ability. It is light-weight thanks to its library approach and efficient because it is based on program transformation. Tor is general enough to mimic search-modifying predicates like ECLiPSe's search/6. Moreover, Tor supports modular composition of search methods and other hooks. The Tor library is already provided and used as an add-on to SWI-Prolog.publisher: Elsevier articletitle: Tor: Modular search with hookable disjunction journaltitle: Science of Computer Programming articlelink: http://dx.doi.org/10.1016/j.scico.2013.05.008 content_type: article copyright: Copyright © 2013 Elsevier B.V. All rights reserved.status: publishe

Exploiting path parallelism in logic programming

This paper presents a novel parallel implementation of Prolog. The system is based on Multipath, a novel execution model for Prolog that implements a partial breadth-first search of the SLD-tree. The paper focusses on the type of parallelism inherent to the execution model, which is called path parallelism. This is a particular case of data parallelism that can be efficiently exploited in a SPMD architecture. A SPMD architecture oriented to the Multipath execution model is presented. A simulator of such system has been developed and used to assess the performance of path parallelism. Performance figures show that path parallelism is effective for non-deterministic programs.Peer ReviewedPostprint (published version

B-LOG: A branch and bound methodology for the parallel execution of logic programs

We propose a computational methodology -"B-LOG"-, which offers the potential for an effective implementation of Logic Programming in a parallel computer. We also propose a weighting scheme to guide the search process through the graph and we apply the concepts of parallel "branch and bound" algorithms in order to perform a "best-first" search using an information theoretic bound. The concept of "session" is used to speed up the search process in a succession of similar queries. Within a session, we strongly modify the bounds in a local database, while bounds kept in a global database are weakly modified to provide a better initial condition for other sessions. We also propose an implementation scheme based on a database machine using "semantic paging", and the "B-LOG processor" based on a scoreboard driven controller

Upside-down Deduction

Over the recent years, several proposals were made to enhance database systems with automated reasoning. In this article we analyze two such enhancements based on meta-interpretation. We consider on the one hand the theorem prover Satchmo, on the other hand the Alexander and Magic Set methods. Although they achieve different goals and are based on distinct reasoning paradigms, Satchmo and the Alexander or Magic Set methods can be similarly described by upside-down meta-interpreters, i.e., meta-interpreters implementing one reasoning principle in terms of the other. Upside-down meta-interpretation gives rise to simple and efficient implementations, but has not been investigated in the past. This article is devoted to studying this technique. We show that it permits one to inherit a search strategy from an inference engine, instead of implementing it, and to combine bottom-up and top-down reasoning. These properties yield an explanation for the efficiency of Satchmo and a justification for the unconventional approach to top-down reasoning of the Alexander and Magic Set methods

Low Size-Complexity Inductive Logic Programming: The East-West Challenge Considered as a Problem in Cost-Sensitive Classification

The Inductive Logic Programming community has considered proof-complexity and model-complexity, but, until recently, size-complexity has received little attention. Recently a challenge was issued "to the international computing community" to discover low size-complexity Prolog programs for classifying trains. The challenge was based on a problem first proposed by Ryszard Michalski, 20 years ago. We interpreted the challenge as a problem in cost-sensitive classification and we applied a recently developed cost-sensitive classifier to the competition. Our algorithm was relatively successful (we won a prize). This paper presents our algorithm and analyzes the results of the competition