Module Specification

The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
1. Module Title Introduction to Artificial Intelligence
2. Module Code COMP111
3. Year Session 2023-24
4. Originating Department Computer Science
5. Faculty Fac of Science & Engineering
6. Semester First Semester
7. CATS Level Level 4 FHEQ
8. CATS Value 15
9. Member of staff with responsibility for the module
Professor F Wolter Computer Science Wolter@liverpool.ac.uk
10. Module Moderator
11. Other Contributing Departments  
12. Other Staff Teaching on this Module
Mrs J Birtall School of Electrical Engineering, Electronics and Computer Science Judith.Birtall@liverpool.ac.uk
13. Board of Studies
14. Mode of Delivery
15. Location Main Liverpool City Campus
    Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
16. Study Hours 30

  12

    10

52
17.

Private Study

98
18.

TOTAL HOURS

150
 
    Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other
19. Timetable (if known)            
 
20. Pre-requisites before taking this module (other modules and/or general educational/academic requirements):

 
21. Modules for which this module is a pre-requisite:

 
22. Co-requisite modules:

 
23. Linked Modules:

 
24. Programme(s) (including Year of Study) to which this module is available on a mandatory basis:

25. Programme(s) (including Year of Study) to which this module is available on a required basis:

26. Programme(s) (including Year of Study) to which this module is available on an optional basis:

27. Aims
 

To provide an introduction to AI through studying search problems, reasoning under uncertainty, knowledge representation, planning, and learning in intelligent systems.
To equip the students with an awareness of the main applications of AI and the history, philosophy, and ethics of AI.

 
28. Learning Outcomes
 

(LO1) Students should be able to identify and describe the characteristics of intelligent agents and the environments that they can inhabit.

 

(LO2) Students should be able to identify, contrast and apply to simple examples the basic search techniques that have been developed for problem-solving in AI.

 

(LO3) Students should be able to apply to simple examples the basic notions of probability theory that have been applied to reasoning under uncertainty in AI.

 

(LO4) Students should be able to identify and describe logical agents and the role of knowledge bases and logical inference in AI.

 

(LO5) Students should be able to identify and describe some approaches to learning in AI and apply these to simple examples.

 

(S1) Problem-solving / critical thinking/ creativity analysing facts and situations and applying creative thinking to develop appropriate solutions.

 

(S2) Literacy application of literacy, ability to produce clear, structured written work and oral literacy - including listening and questioning.

 
29. Teaching and Learning Strategies
 

Teaching Method 1 - Lecture
Description: Students will be expected to attend three hours of formal lectures in a typical week.
Attendance Recorded: Yes
Notes: Students are expected to spend at least one hour per week for completion of practical exercises.
Unscheduled Directed Student Hours (time spent away from the timetabled sessions but directed by the teaching staff): 10

Teaching Method 2 - Tutorial
Description: One hour of tutorials accompany lectures in a typical week
Attendance Recorded: Yes

Standard on-campus delivery
Teaching Method 1 - Lecture
Description: Mix of on-campus/on-line synchronous/asynchronous sessions
Teaching Method 2 - Tutorial
Description: On-campus synchronous sessions

 
30. Syllabus
   

History of AI including recent developments (2 lectures): the early history of AI including the calculus ratiocinator, the Church-Turing Thesis, the significance of the Dartmouth Conference, the development of expert systems, the fifth generation computer project, the AI winter, and the development of Deep Blue. Recent developments will be introduced by discussing, for example, IBM's Watson, AlphaGo, and the DARPA Grand Challange. The examples of recent developments are revisited to motivate the introduction of search problems, reasoning under uncertainty, knowledge representation, and learning in subsequent lectures.

Problem-Solving Through Search (8 lectures): Problem formulation; uninformed search strategies; informed search strategies; constraint satisfaction problems; adversarial search. Reasoning under

Uncertainty (9 lectures): Probability in AI; axioms of probability; joint distribution; independence; Bayes' rule; Bayesian networks.

Knowledge Repre sentation (4 lectures): Logic; logical agents; knowledge engineering; inference;  planning; Goedel's incompleteness theorem.

Learning (4 lectures): Different forms of learning; reinforcement learning. Philosophy and ethics of AI (3 lectures): Introduction to the questions 'Can a machine act intelligently?' and 'Can a machine have mental states?'; in particular, the Turing Test and Searle's Chinese room argument are introduced. Ethics of AI is introduced by discussing machine ethics and weaponization of AI.

 
31. Recommended Texts
  Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.
 

Assessment

32. EXAM Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
  (111) Final exam There is a resit opportunity. Standard UoL penalty applies for late submission. This is an anonymous assessment. Assessment Schedule (When) :Semester 1 120 70
33. CONTINUOUS Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
  (111.2) Class test 1 There is a resit opportunity. Standard UoL penalty applies for late submission. This is an anonymous assessment. Assessment Schedule (When) :Semester 1 0 10
  (111.3) Class test 2 There is a resit opportunity. Standard UoL penalty applies for late submission. This is an anonymous assessment. Assessment Schedule (When) :Semester 1 0 10
  (111.1) Assessed homework There is a resit opportunity. Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Semester 1 0 10