This document provides information for students taking an Introduction to Artificial Intelligence course, including details about tutors, class schedule, course content, assignments, assessment, and recommended reading. Over 12 weeks, topics will include search techniques, logic, knowledge representation, machine learning algorithms, and developing rule-based expert systems. Students will complete 4 assignments accounting for 40% of the grade, and take a final exam worth 60%. Total course hours are estimated at 300, with 69 hours of class time.

1. Introduction to Artificial Intelligence
Information for students 2000/2001
(Level 2/Level 3)
Course Tutor(s):
Name Office Phone Email
Venky Shankararaman LC267 4351 V.Shankararaman
Neil Davey LB220 4310 N.Davey
Dave Smith D209 4341 D.E.Smith
Vivian Ambrosiadou B301 4347 B.V.Ambrosiadou
Amanda Derrick LC255 4369 A.J.Derrick
Course web page: http://homepages.feis.herts.ac.uk/~2com0007
Class contact arrangements:
2 hours lecture
1 hour tutorial
Course Delivery Plan:
Week Title Activity Material Coursework Time
02 Problems and Search Exercises + Handout 1 10 hours
2/10 Get BFS code working Chapter 1, Chapter 3,
Chapter 10 Luger
03 Uninformed Methods Exercises + Handout 1 Coursework 1 10 hours
09/10 Work on coursework Chapter 3 Luger Handed Out
04 Algorithms Exercises + Handout 1 Chapter 3 Luger 10 hours
16/10 Work on coursework
05 Informed methods and Check Point: structured Handout 1 Chapter 4, 10 hours
23/10 heuristic search search. — check ID Luger
06 Hill Climbing and Use provided code to Chapter 4 Dean, Allen, 10 hours
30/10 Simulated Annealing run some SA Aloimonos
experiments
07 Genetic Algorithms Run some SA Chapter 15 Luger 10 hours
06/11 experiments
08 Adversarial Search Exercises Luger Chapter 4. Section 10 hours
13/11 4.3
09 Adversarial Search Luger Chapter 4. Section Coursework 1 10 hours
20/11 4.3 Completed
10 Overview Examples sheet Luger 2.0 — 2.2 and 10 hours
27/11 Predicate Logic, syntax and handout
semantics
11 Predicate Logic Example sheet, on Luger 2.3, 6.0 and handout 10 hours
04/12 Single Proofs by resolution. unification and
Unification resolution.
Horn Clauses and Prolog
12 Theorem Proving in Prolog Example sheet, on Luger 6.1 and handout 10 hours
11/12 Non declarative semantics Prolog search trees/
of Prolog PROLOG practical
16 Semantic Networks Example sheet on Luger 9.0-9.3 + handout Coursework 2 10 hours
08/01 Conceptual Graphs graphical handed out
representations of
knowledge / Prolog
practical

2. Week Title Activity Material Coursework Time
17 Frames Example sheet on Luger 9.4 — 9.5 10 hours
15/01 Type Hierarchy Frames + handout
Prolog practical
19 Human Info. Processing Review some expert Handout 1. Coursework 2 10 hours
29/01 and Introduction to expert system applications Luger Chapter 6, Sections completed
systems 6.0, and 6.1
20 Rule-based systems Exercise on writing Handout 2. Coursework 3 10 hours
05/02 rules Luger Chapter 5, Section hand out
5.3 and Chapter 6, Section
6.2
21 Phases in developing a Exercise on modeling Handout 3. 10 hours
12/02 KBS
22 CLIPS Exercise on CLIPS + CLIPS user guide 10 hours
19/02 work on coursework
23 CLIPS Exercise on CLIPS + CLIPS user guide 10 hours
26/02 work on coursework
24 Modelling Learning- Handout 1, Coursework 3 5 hours
05/03 Methods, Approaches and Chapter 13 Luger completed
Terms, ID3 Algorithm Coursework 4
Handed out
25 More on Quinlan’s ID3 Understanding the ID3 Handout 2, Luger, Chapter Laboratory work 15 hours
12/03 Algorithm and software algorithm and 13
information theory by
working out specific
examples given by the
lecturer
26 Version Space Search Practice on algorithms Handout 3, Luger, Chapter 12 hours
19/03 General to Specific Search, by using specific 13
Specific to General Search, examples given by the
lecturer, understanding
of the coursework
27 Machine Learning- Practice on algorithms Handout 4, Luger, Chapter Coursework 4 12 hours
26/03 Winstons Algorithms and 13 completed
Candidate Elimination
Algorithm
31 Machine Learning-
23/04 Revision and coursework
feedback
32
Revision
30/04
Assessment method: 40 % Coursework 60% Examination
The assessment for the Level 2 and Level 3 are separate with some shared components.
Pass conditions: Pass overall
In-course assignments:
CW1
Date set: w/b 9 October
Submission date: Friday 24 November by 3 pm at FEIS reception
Percentage of total assessment: 16
Group or Individual: Group (pairs)
Topic: Search

3. Target date for return of marked work: w/b 8 January
CW2
Date set: w/b 8 January
Submission date: Friday 2 February by 3pm at FEIS reception
Percentage of total assessment: 8%
Group or Individual: Individual
Topic: Logic
Target date for return of marked work: w/b 26 March
CW3
Date set: w/b 5 February
Submission date: Friday 9 March by 3pm at FEIS reception
Percentage of total assessment: 8%
Group or Individual: Group (pairs)
Topic: Rule-Based Systems
Target date for return of marked work: w/b 23 April
CW4
Date set: w/b 12 March
Submission date: Friday 30 March by 3pm at FEIS reception
Percentage of total assessment: 8%
Group or Individual: Group (pairs)
Topic: Machine Learning
Target date for return of marked work: w/b 23 April
Study time:
Total: 300 hours
of which
Class contact: 69
Assessment: 75
Directed study outside class time: 80
Other activities (non-assessed): 76
eg. reading, library investigations, practical exercises or revision for
examination
Recommended reading
Essential reading
Lecture hand-outs
Additional reading
Artificial Intelligence, Theory and Practice. Thomas Dean, James Allen and John Alloimonos,
Benjamin Cummings, 1995.
Luger G F and Stubblefield W A. Artificial Intelligence: Structures and Strategies for
Complex Problem Solving. 1998. Addison Wesley Longman, Inc.