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Next: MC325 Relativity and Electromagnetism
Up: Year 3
Previous: MC316 Parallel and Distributed Computing
MC317 Artificial Intelligence and Knowledge Engineering
| Credits: 20 |
Convenor: Dr. N. Yoshida |
Semester: 2 |
| Prerequisites: |
essential: MC103, MC104, MC111, MC214 |
|
| Assessment: |
Continuous assessment: 30% |
Three hour exam in May/June: 70% |
| Lectures: |
36 |
Problem Classes: |
12 |
| Tutorials: |
none |
Private Study: |
90 |
| Labs: |
12 |
Seminars: |
none |
| Project: |
none |
Other: |
none |
| Surgeries: |
none |
Total: |
150 |
Explanation of Pre-requisites
Students on this module will need basic knowledge of programming,
computer science mathematics, and information systems. This knowledge
is supplied by the modules MC103, MC104, MC 115 and MC111.
They also need some experience of Logic and Prolog, as supplied by
MC214, Logic Programming.
Course Description
This module first provides an integrated introduction to Artificial
Intelligence based on logical approach, emphasising practical techniques
and Prolog implementations.
Knowledge Engineering is a technique to elucidate knowledge
from human experts, to store it in the form of database, and
to design an expert system.
An expert system is a knowledge-based program which imitates the
behaviour of a human expert in some problem domain. Expert systems
have increased in popularity over the years, and have been used in
such areas as medical diagnosis, mineral exploration, computer
system design, and financial credit control.
Aims
The module introduces students to the main principles underlying
artificial intelligence and knowledge engineering.
First by using the Prolog language,
students learn the basic AI technology and semantics of
knowledge engineering.
Secondly students will learn the benefits arising from the use
of AI and expert systems in business.
Finally students will learn how to implement elementary
robot control systems and expert systems.
By the end of the module students will be able to solve
the basic AI problems in Prolog, to construct simple
rule-based expert systems in Prolog, and to learn
the real application domain of AI and knowledge engineering technology.
Objectives
- To understand the historical development of AI technology.
- To learn constraint logic programming.
- To understand the fundamental concepts of semantics of knowledge based system.
- To be able to design robot control and
expert systems, and implement them in Prolog.
- To learn what knowledge engineering is
and what a knowledge engineer does.
Transferable Skills
- The ability to understand logics and semantics of
knowledge engineering.
- 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.
Syllabus
Advanced Prolog Programming: the breath-first search and the depth-first
strategies; system predicates; operators; second order programming;
constraint programming.
Representation and Reasoning Systems: assumptions; datalog; semantics;
questions and answers; proofs; the language with function symbols;
database and recursion; top-down and bottom-up procedures; soundness
and completeness.
Knowledge engineer concepts; production system concepts;
expert system concepts;
implementing production systems
and the explanatory interface in Prolog; dealing with uncertainty:
certainty factors, implementing certainty factors in Prolog, alternative
approaches.
Applied AI and knowledge engineering;
history of AI and expert systems; knowledge engineering;
software for building expert system; robot control programming;
AI applications, the market and the future.
Reading list
Essential:
I. Bratko,
Prolog, Programming for Artificial Intelligence, 3rd edition,
Addison-Wesley.
David Poole, Alan Mackworth and Randy Goebel,
Computational Intelligence: Logical Approach,
Oxford.
Recommended:
K. Darlington,
The Essence of Expert Systems,
Prentice Hall.
G. Luger and W. Stubblefield,
Artificial Intelligence, Structures and Strategies for Complex
Problem Solving, 2nd edition,
Benjamin-Cummings.
Background:
A. Cawsey,
The Essence of Artificial Intelligence,
Prentice Hall.
Details of Assessment
The coursework for the continuous assessment consists of four worksheets:
- Survey of AI Application
(to learn about what AI applications exist,
and to think about what intelligence could mean) (written work: 1 week).
- Search Technique and Constraint Programming
(laboratory based work: 2 week).
- Expert System Shell Programming (laboratory based work: 2 weeks).
- Theory of Knowledge Engineering (written work: 1 week).
- A Mini-Project: Advanced Expert Systems
or Robotics Programming (laboratory based work: 2 weeks).
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.
Next: MC325 Relativity and Electromagnetism
Up: Year 3
Previous: MC316 Parallel and Distributed Computing
Author: S. J. Ambler, tel: +44 (0)116 252 3884
Last updated: 2001-09-20
MCS Web Maintainer
This document has been approved by the Head of Department.
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