Department of Mathematics & Computer Science

MC315 Expert Systems and Deductive Databases

Explanation of Pre-requisites

They also need some experience of Logic and Prolog, as supplied by MC214, Logic Programming.

Course Description

The expert systems which we consider on this module are based on rules. This gives the connection with the idea of a deductive database, which extends the relational model by adding rules.

Aims

By the end of the module students will be able to construct simple rule-based expert systems in Prolog, will be able to learn the knowledge engineering technology in the real application domain, and will be familiar with the semantics of deductive databases and their query languages.

Objectives

• To be able to design expert systems and implement them in Prolog.
• To learn what knowledge engineering is and what a knowledge engineer does.
• To understand the fundamental concepts of Deductive Databases.

Transferable Skills

• The ability to devise a knowledge representation scheme in a given domain.
• The ability to implement a rule-based system for a given problem domain.
• An understanding of integrity constraints and query evaluation for deductive databases.

Syllabus

Expert system concepts; production system concepts; implementing production systems and the explanatory interface in Prolog; dealing with uncertainty: certainty factors, implementing certainty factors in Prolog, alternative approaches.

Applied expert system; AI and expert systems; knowledge engineering; software for building expert system; human-computer interaction issues for expert systems; the expert system development life-cycle; applications, the market and the future.

The relational model of databases, relational calculus; model-theoretic account of integrity constraints; model-theoretic account of relational calculus queries; relational algebra, equivalence between relational algebra and relational calculus, query valuation in relational databases; the proof-theoretic account of databases, queries, and integrity constraints; deductive database concepts, hierarchical, definite, and stratified databases, extension of model-theoretic and proof-theoretic accounts to dbs; query evaluation in hierarchical databases; query evaluation in definite databases: computing the least Herbrand model, associated fixpoint theorems.

Reading list

Essential:

I. Bratko, Prolog, Programming for Artificial Intelligence, 2nd edition, Addison-Wesley.

K. Darlington, The Essence of Expert Systems, Prentice Hall.

Recommended:

G. Luger and W. Stubblefield, Artificial Intelligence, Structures and Strategies for Complex Problem Solving, 2nd edition, Benjamin-Cummings.

S. Abiteboul, R. Hull and V. Vianu, Foundations of Databases, the Logical Level, Addison-Wesley.

Background:

P. Jackson, Introduction to Expert System, Addison-Wesley.

L. Sterling and E. Shapiro, The Art of Prolog, 2nd edition, M.I.T.

T. Van Le, Techniques of Prolog Programming, Wiley.

Background:

A. Thayse et al., From Modal Logic to Deductive Databases, Wiley.

Details of Assessment

1.

Expert Systems: Production Systems, Control Strategies, User Interface in Prolog (laboratory based work).

2.

Uncertainty Expert Systems in Prolog (laboratory based work).

3.

Advanced Expert Systems by using an expert system shell (laboratory based work).

4.

Deductive Database (this sheet is purely theoretical).

The written Midsummer examination contains six questions, and the best four questions will be taken into account in determining the mark. The examination will test candidates' knowledge of both theoretical and practical issues.

Author: S. J. Ambler, tel: +44 (0)116 252 3884
Last updated: 10/4/2000
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