DATABASE ACCESS REQUIREMENTS OF KNOWLEDGE-BASED SYSTEMS

Knowledge bases constitute the core of those Artificial Intelligence programs which have come to be known as Expert Systems. An examination of the most dominant knowledge representation schemes used in these systems reveals that a knowledge base can, and possibly should, be described at several levels using different schemes, including those traditionally used in operational databases. This chapter provides evidence that solutions to the organization and access problem for very large knowledge bases require the employment of appropriate database management methods, at least for the lowest level of description -- the facts or data. We identify the database access requirements of knowledge-based or expert systems and then present four general architectural strategies for the design of expert systems that interact with databases, together with specific recommendations for their suitability in particular situations. An implementation of the most advanced and ambitious of these strategies is then discussed in some detail.Information Systems Working Papers Serie

Kolmogorov Complexity in perspective. Part II: Classification, Information Processing and Duality

We survey diverse approaches to the notion of information: from Shannon entropy to Kolmogorov complexity. Two of the main applications of Kolmogorov complexity are presented: randomness and classification. The survey is divided in two parts published in a same volume. Part II is dedicated to the relation between logic and information system, within the scope of Kolmogorov algorithmic information theory. We present a recent application of Kolmogorov complexity: classification using compression, an idea with provocative implementation by authors such as Bennett, Vitanyi and Cilibrasi. This stresses how Kolmogorov complexity, besides being a foundation to randomness, is also related to classification. Another approach to classification is also considered: the so-called "Google classification". It uses another original and attractive idea which is connected to the classification using compression and to Kolmogorov complexity from a conceptual point of view. We present and unify these different approaches to classification in terms of Bottom-Up versus Top-Down operational modes, of which we point the fundamental principles and the underlying duality. We look at the way these two dual modes are used in different approaches to information system, particularly the relational model for database introduced by Codd in the 70's. This allows to point out diverse forms of a fundamental duality. These operational modes are also reinterpreted in the context of the comprehension schema of axiomatic set theory ZF. This leads us to develop how Kolmogorov's complexity is linked to intensionality, abstraction, classification and information system.Comment: 43 page

Pengines: Web Logic Programming Made Easy

When developing a (web) interface for a deductive database, functionality required by the client is provided by means of HTTP handlers that wrap the logical data access predicates. These handlers are responsible for converting between client and server data representations and typically include options for paginating results. Designing the web accessible API is difficult because it is hard to predict the exact requirements of clients. Pengines changes this picture. The client provides a Prolog program that selects the required data by accessing the logical API of the server. The pengine infrastructure provides general mechanisms for converting Prolog data and handling Prolog non-determinism. The Pengines library is small (2000 lines Prolog, 150 lines JavaScript). It greatly simplifies defining an AJAX based client for a Prolog program and provides non-deterministic RPC between Prolog processes as well as interaction with Prolog engines similar to Paul Tarau's engines. Pengines are available as a standard package for SWI-Prolog 7.Comment: To appear in Theory and Practice of Logic Programmin