Computer Science, Prifysgol Cymru Aberystwyth University of Wales
CS36210 (1995-96 session)
Artificial Intelligence Concepts
Brief Description
Artificial Intelligence (AI) has made many important
contributions to computer science and most experts believe AI
techniques will become increasingly important. This module
introduces students to the fundamental concepts of AI. Key issues
including knowledge representation, reasoning and learning are
addressed both theoretically and practically.
Aims, Objectives, Syllabus, Booklist
Further Details
- Number of lectures
- 24
- Number of seminars/tutorials
- 0
- Number of practicals
- 6 x 1-hour
- Coordinator
- Dr. Fred Long
- Other staff involved
- Not yet known
- Pre-requisites
-
CS21020
/C210
- Co-requisites
- None
- Incompatibilities
-
CS26210
- Assessment
- Assessed coursework - 20%
Written exam -
80%
- Timing
- This module is offered only in Semester 1
Aims
Artificial Intelligence is the study of computer systems which
can perform the sort of tasks that are usually associated with
human intelligence. Examples are: chess playing, pattern
recognition, speech understanding and problem solving. The aim of
this module is to introduce the main ideas and current problems
in Artificial Intelligence including the key concepts of knowledge
representation and reasoning, expert systems and learning.
Students will be required to implement these concepts using an
Artificial Intelligence programming language.
Objectives
On successful completion of this module students should:
-
understand the importance of knowledge representation and
their role in Artificial Intelligence systems;
-
understand the use search in the solution of problems such
as path planning and game playing;
-
be familiar with the principle techniques used in
implementing machine learning;
-
understand the fundamentals of first generation expert
system technology, and the conceptual basis of current
attempts to overcome its limitations;
-
recognise the distinction between symbolic approaches to
Artificial Intelligence and sub-symbolic approaches;
-
be capable of implementing fundamental Artificial
Intelligence algorithms in the high level Artificial
Intelligence language POP-11.
Syllabus
-
Introduction - 1 Lecture, 6 Practicals
-
The origins of Artificial Intelligence; 4 definitions
of Artificial Intelligence. Characterising good and bad problem
domains.
-
Introduction to POP-11 - 3 Lectures
-
Poplog environment, TEACH, HELP, basic POP-11, list
processing, recursion, advanced features.
-
Knowledge
Representation - 3 Lectures
-
Issues in knowledge representation: representation
adequacy; inferential adequacy; inferential efficiency;
acquisitional efficiency. Knowledge Representation formalisms:
propositional and predicate logic, procedural representations;
semantic nets; frame-based representations.
-
Search - 2 Lectures
-
The role of search in Artificial Intelligence. Search
strategies: basic search; heuristic search; and game playing.
Two workshops implementing depth- and breadth-first search,
heuristic search and search space pruning.
-
Expert
Systems - 2 Lectures
-
Characterising first generation expert systems.
Expert system structure: knowledge representation; control
strategies. Limitations of current expert system technology.
-
Learning - 3 Lectures
-
The role of learning in Artificial Intelligence.
Symbollic approaches to learning: rote learning; Winston's arch
learning program; version spaces and CYC.
-
Qualitative
and Model-Based Reasoning - 4 Lectures
-
Techniques and methods used to provide second
generation expert systems. Qualitative vs. quantitative
representation and reasoning. Modelling physical domains and
naive physics. Structure, behaviour, function and purpose.
Models for simulation and diagnosis.
-
The Physical
Symbol Hypothesis - 1 Lecture
-
Strong and weak Artificial Intelligence -
philosophical interlude.
-
Neural
Nets - 2 Lectures
-
Simulated neural networks. Hill climbing and back
propagation. Characteristics of back propagation.
-
Genetic Algorithms - 1 Lecture
-
Chromosomes and hereditary traits; survival of the
fittest; crossover; the rank method.
Booklist
It is considered essential to purchase the following
-
E. Rich and K. Knight.
Artificial Intelligence.
McGraw Hill, 2nd. edition, 1991.
Students are likely to need ready access to the following
-
Ramsay, Barrett, and Sloman.
POP-11: A Practical Language for Artificial Intelligence.
Ellis Horwood, 1985.
-
Charniak and McDermott.
Introduction to Artificial Intelligence.
Addison Wesley, 1985.
-
P. H. Winston.
Artificial Intelligence.
Addison Wesley, 3rd. edition, 1992.
The following should be consulted for different approaches or for further information
-
S. C. Shapiro.
Encyclopedia of Artificial Intelligence.
Addison-Wesley, 1992.
Version 4.1
Syllabus
John Hunt Departmental Advisor
jjh@aber.ac.uk
Dept of Computer Science, UW Aberystwyth (disclaimer)