ElipSys is a parallel programming system based on logic being developed at ECRC. The aim of the project is to support the development and delivery of large decision support systems. It integrates three of the most important technologies required to support such systems: constraint satisfaction, tight database coupling and parallel evaluation. The project addresses issues at all levels: from language design to implementation. Of particular note is the work to improve the expressiveness and declarativeness of logic programming, and the design of an execution model that is appropriate to a range of parallel machine architectures, from shared memory to distributed memory. A prototype version of ElipSys is running on the Sequent Symmetry and a network of SUN workstations running the MACH operating system. Prototypes of a number of commercially and technologically important applications have been implemented using ElipSys by a number of end-user application development organizations.

ElipSys is a programming system supporting a constraint logic programming (CLP) language and OR-parallel execution. These two features complement each other: CLP programming eases the writing of efficient search programs while OR-parallelism allows one to attain quasi-linear or super-linear speed-ups when the programs are executed on parallel machines. The speed-ups come without significant additional programming effort. This paper gives the rationale behind this combination, explains how it influences the design of the language and the implementation, and gives results providing evidence for the synergy of these two paradigms.

The APPLAUSE ESPRIT Project is building major applications using the ElipSys parallel constraint logic programming system developed at ECRC. Two major aims of the project are to advance the state of the art in four commercially significant application areas and to promote the use of ElipSys-like languages among applications developers. This brief paper gives an outline of ElipSys and an overview of the applications being developed within the APPLAUSE Project.

We are all very conscious of living through a revolution — one in which the industrial society is being superseded by the information society. Every day brings new evidence of the breakneck pace of the changes that are currently underway. But while broad awareness may be unavoidable, understanding is not so easy. Both the p ace of the revolution and its multi-faceted nature make it difficult to gain a clear perspective. But here the new science of complexity can perhaps help. It provides a coherent theory that is directly applicable to the emerging society, potentially providing new insights and new understanding. This paper examines several facets of the current revolution from a complexity perspective, and suggests that the relationship between the emerging science and the emerging society will be a rich one.

MaTourA is a tourist advisory system about Greece that is being implemented in the parallel constraint logic programming language ElipSys. The purpose of MaTourA is to facilitate the work carried out in travel agencies by providing an interactive way to construct personalized tours, select predefined package tours and handle the underlying touristic information. The system has been designed as a set of high-level interacting agents. In this direction, the ElipSys language was extended with the appropriate features to support the development of multi-agent systems.

Many areas of scientific endeavour can be characterized as the attempt to provide a consistent interpretation of a broad range of heterogeneous data and theories. In the area of protein structure prediction, for example, there are many types of diverse mutually constraining data and theories of protein structural organization that need to be integrated in order to produce a single consistent prediction (or set of predictions) of the protein structure from the experimentally derived amino acid sequence data. Understanding the role and function of proteins in the control of cell growth is an important part of contemporary cancer research. Protein structure prediction is immensely (combinatorially) complex, and traditional computational approaches to problems such as this have been based on the ''generate and test' paradigm in which hypotheses are first generated and then tested against any relevant constraints. In this paper we demonstrate the benefits of a new approach to solving large constrained combinatorial problems which uses the 'constrain-and-generate' paradigm and the ElipSys parallel constraint logic programming system. In ElipSys, constraints are used for a priori pruning of the search tree while parallelism enhances the efficiency of the remaining search. Initial results show several orders of magnitude increase in performance over a sequential logic programming (Prolog) approach. The improved performance can be attributed to the complementary actions of the constraint handling and support for parallelism in the ElipSys runtime system. Taken together, the additional performance and new knowledge representation techniques made possible using ElipSys significantly extend the range and complexity of scientific problems that can be addressed using logic programming languages.

In this paper, two programs are described (CBS1e and CBS2e). These are implemented in the parallel constraint logic programming language ElipSys. These predict protein alpha/beta-sheet and beta-sheet topologies from secondary structure assignments and topological folding rules (constraints). These programs illustrate how recent developments in logic programming environments can be applied to solve large-scale combinatorial problems in molecular biology. We demonstrate that parallel constraint logic programming is able to overcome some of the important limitations of more established logic programming languages i.e. Prolog. This is particularly the case in providing features that enhance the declarative nature of the program and also in addressing directly the problems of scaling-up logic programs to solve scientifically realistic problems. Moreover, we show that for large topological problems CBS1e was approximately 60 times faster than an equivalent Prolog implementation (CBS1) on a sequential device with further performance enhancements possible on parallel computer architectures. CBS2e is an extension of CBS1e that addresses the important problem of integrating the use of uncertain (weighted) protein folding constraints with categorical ones, through the use of a cost function that is minimized. CBS2e achieves this with a relatively minor reduction of performance. These results significantly extend the range and complexity of protein structure prediction methods that can reasonably be addressed using AI languages.

The EP2025 EDS project, which is developing a highly parallel information server that supports established high-value interfaces, is discussed. The motivation for the project, the architecture of the system, and the design and application of its database and language subsystems are described. The Elipsys logic programming language, its advanced applications, EDS Lisp, and the Metal machine translation system are examined.<>

Comprises papers based on an international conference held at Imperial College, London, July 1989. Topics covered include neural networks, robotics, image understanding, parallel implementations of logic languages, and parallel implementation of Lisp. Many of the papers here detail use of the INMOS transputer, and the Communicating Process Architecture on which INMOS was founded. But the theme is application of parallelism in a general way, especially in artificial intelligence.