Module Details |
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. |
Title | Introduction to Artificial Intelligence | ||
Code | COMP111 | ||
Coordinator |
Professor F Wolter Computer Science Wolter@liverpool.ac.uk |
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Year | CATS Level | Semester | CATS Value |
Session 2021-22 | Level 4 FHEQ | First Semester | 15 |
Aims |
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To provide an introduction to AI through studying search problems, reasoning under uncertainty, knowledge representation, planning, and learning in intelligent systems. |
Learning Outcomes |
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(LO1) Students should be able to identify and describe the characteristics of intelligent agents and the environments that they can inhabit. |
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(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. |
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(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. |
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(LO4) Students should be able to identify and describe logical agents and the role of knowledge bases and logical inference in AI. |
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(LO5) Students should be able to identify and describe some approaches to learning in AI and apply these to simple examples. |
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(S1) Problem-solving / critical thinking/ creativity analysing facts and situations and applying creative thinking to develop appropriate solutions. |
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(S2) Literacy application of literacy, ability to produce clear, structured written work and oral literacy - including listening and questioning. |
Syllabus |
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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. |
Teaching and Learning Strategies |
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Teaching Method 1 - Lecture Teaching Method 2 - Tutorial Due to Covid-19, in 2021/22, one or more of the following delivery methods will be implemented based on the current local conditions. (b) Fully online delivery and assessment (c) Standard on-campus delivery |
Teaching Schedule |
Lectures | Seminars | Tutorials | Lab Practicals | Fieldwork Placement | Other | TOTAL | |
Study Hours |
30 |
12 |
10 |
52 | |||
Timetable (if known) | |||||||
Private Study | 98 | ||||||
TOTAL HOURS | 150 |
Assessment |
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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 minutes. | 60 | ||||
CONTINUOUS | Duration | Timing (Semester) |
% of final mark |
Resit/resubmission opportunity |
Penalty for late submission |
Notes |
(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 | 8 hours of tutorials | 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 | 30 minutes | 10 | ||||
(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 | 30 minutes | 10 | ||||
(111.4) Class test 3 There is a resit opportunity. Standard UoL penalty applies for late submission. This is an anonymous assessment. Assessment Schedule (When) :Semester 1 | 8 hours of tutorials | 10 |
Recommended Texts |
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Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module. |