06-11352 AI Techniques A

Manfred Kerber et al. 10 credits in Semester 1



Learning Outcomes

On completion of this module, the student should be able to:

Prerequisites and Corequisites

No Prerequisites, 06-11353 AI Techniques B must be taken in conjunction with this module (linked module).

Teaching Methods

3 hrs a week of lectures and exercise classes.


80% as a 3 hr written examination, 20% as continuous assessment, divided equally between this module and 06-11353 AI Techniques B. Re-assessment is 100% by exam. The continuous assessment will be in form of two in-class tests, scheduled for week 7, 14 November 2000, and week 11, 12 December 2000, 9:00-10:00 in LR7 each.

Recommended Books

Title Author(s) Publisher, Date Comments
Introduction to Expert Systems, 2nd Edition or 3rd Edition Peter Jackson Addison-Wesley 1990 Further Reading
Artificial Intelligence: A New Synthesis Nils J. Nilsson Morgan Kaufmann 1998 These are some of the general introductions to AI. To look into these and some others and to buy one of them is advisable.
Artificial Intelligence - A Modern Approach Stuart Russell & Peter Norvig Prentice Hall 1995
Artificial Intelligence, 3rd Edition Patrick Henry Winston Addison-Wesley, 1992
Artificial Intelligence, 2nd Edition Elaine Rich & Kevin Knight McGraw Hill 1991

Detailed Syllabus and Relevant Links

The slides for the Monday lectures and the worksheets will be made available on-line after they have been handed out in the lecture. Please DO NOT PRINT THEM OUT. First check for spare and reference copies which can be found in the School's library. DO NOT WASTE PRINTER RESOURCES.

Week Lecture 1   LR7, Mo 10:00-11:00 Lecture 2   LR7, Tu 15:00-16:00 Exercises   LR7, Tu  9:00-10:00
1 Induction Week, one Lecture, Tuesday, 9:00-10:00, LR7 on "AI - The Dream" (gzipped PostScript, pdf file) Worksheet 1 (gzipped PostScript, pdf file)
2 AI - The Roots (Logic, computation, brain science) (gzipped PostScript, pdf file) The birth of AI (Dartmouth, Turing Test, Church's thesis) (Jeremy Wyatt) Worksheet 2 (gzipped PostScript, pdf file)
3 What is AI (subfields, why one field, common techniques) (gzipped PostScript, pdf file) Studying intelligent systems (Jeremy Wyatt) Worksheet 3 (gzipped PostScript, pdf file)
4 Search (gzipped PostScript, pdf file) The experimental psychology of simulative reasoning (Donald Peterson) Worksheet 4 (gzipped PostScript, pdf file)
5 Knowledge Representation I (Data vs Knowledge, Foundations) (gzipped PostScript, pdf file) Vision (Ela Claridge) Worksheet 5 (gzipped PostScript, pdf file)
6 Knowledge Representation II (Frames) (gzipped PostScript, pdf file) Natural Language (Peter Coxhead) Worksheet 6 (gzipped PostScript, pdf file)
7 Knowledge Representation III (Semantic Nets, Inheritance) (gzipped PostScript, pdf file) Reasoning (John Barnden) Worksheet 7 (gzipped PostScript, pdf file)
8 Knowledge Representation IV (Rules I, Recognise-Act-Cycle, Matching) (gzipped PostScript, pdf file) Rules II (Rete, Conflict resolution) (Manfred Kerber) Worksheet 8 (gzipped PostScript, pdf file)
9 Knowledge Elicitation (gzipped PostScript, pdf file) Shakey (Jeremy Wyatt) Worksheet 9 (gzipped PostScript, pdf file)
10 Uncertainty Treatment (gzipped PostScript, pdf file) Theorem Proving (gzipped PostScript, pdf file(Manfred Kerber) Worksheet 10 (gzipped PostScript, pdf file)
11 Limitations of AI Systems (Dreyfus, Searle, Penrose) (gzipped PostScript, pdf file) Philosophical Issues (Aaron Sloman) Worksheet 11 (gzipped PostScript, pdf file)

A naive POP11-implementation of an expert system can be found here.


Degree Regulations Aims Outcomes Prerequisites Teaching Methods Assessment Recommended Books Detailed Syllabus Relevant Links

Maintained by: Manfred Kerber, School of Computer Science, The University of Birmingham
Last update: 5.12.2000