Knowledge base management systems (KBMS) are designed to efficiently retrieve and manipulate large shared knowledge bases. A significant subclass of KBMS consisting of a combination of logic programming and database is often called a logic oriented knowledge base system (LOKBS). These systems must possess considerable processing and I/O capabilities so many approaches have been taken to the improvement of their performance. In this paper we review the current performance enhancing hardware approaches for LOKBS. We include parallelism, both in processing and I/O, algorithms, caching, and physical data organizations.

This paper describes the interfacing problem that arose in a Product Formulation expert system written in LISP that had to be interfaced to data in a relational database running on a separate mainframe computer. It surveys the different forms of coupling that are possible and emphasizes the advantages of tight navigational coupling over the more popular set-based coupling. It describes how Prolog was used to overcome the interfacing problems and to provide a customized front end to an end user, based on a navigational interface. It reviews the techniques of using Prolog and the likely obstacles, together with a look forward to databases using Frames or Objects.

The application area of knowledge-based expert systems is currently providing the main stimulus for developing powerful, parallel computer architectures. Languages for programming knowledge-based applications divide into four broad classes: Functional languages (e.g. LISP), Logic languages (e.g. PROLOG), Rule-Based languages (e.g. OPS5), and, what we refer to as self-organizing networks (e.g. BOLTZMANN machines).

Despite their many differences, a common problem for all language classes and their supporting machine architectures is parallelism: how to de-compose a single computation into a number of parallel tasks that can be distributed across an ensemble of processors. The aim of this paper is to review the four types of language for programming knowledge-based expert systems, and their supporting parallel machine architectures. In doing so we analyze the concepts and relationships that exist between the programming languages and their parallel machine architectures in terms of their strengths and limitations for exploiting parallelization.