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- UCAS institution code
BSc Computer Science and Mathematics
Year of entry: 2023
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Course unit details:
Introduction to AI
|Unit level||Level 2|
|Teaching period(s)||Semester 1|
|Offered by||Department of Computer Science|
|Available as a free choice unit?||No|
The Unit aims to make students familiar with the basic concepts and techniques of Artificial Intelligence. It provides the knowledge and understanding that underpins later course units in the subject taught in the Department.
|Unit title||Unit code||Requirement type||Description|
|Mathematical Techniques for Computer Science||COMP11120||Pre-Requisite||Compulsory|
COMP11120 (not a pre-requisite for CM)
A student completing this course should:
- be able to implement basic search- and planning-algorithms from Artificial Intelligence, and apply them to real-world problems;
- be able to apply first-order logic to model physical situations and reason about the effects of actions,
- to appreciate the limitations of logic and to select appropriate responses to these limitations;
- be able to develop formal ontologies to represent knowledge in different domains;
- be able to select and apply the principal models of uncertainty employed in Artificial Intelligence in concrete problem-solving situations;
- be able to solve the problem of sensor integration, and to implement simultaneous localization and mapping in robotics;
- be able to apply techniques for representing (qualitative) temporal and spatial information in Artificial Intelligence;
- have an appreciation of the central philosophical problems connected with artificial intelligence.
On the successful completion of the course, students will be able to:
- ILO 1 To be able to implement basic search- and planning-algorithms from Artificial Intelligence, and apply them to real-world problems.
- ILO 2 To be able to apply first-order logic to model physical situations and to reason about the effects of actions.
- ILO 3 To appreciate the limitations of logic and to be able to select appropriate responses to these limitations.
- ILO 4 To be able to develop formal ontologies to represent knowledge in different domains.
- ILO 5 To be able to select and apply the principal models of uncertainty employed in Artificial Intelligence in concrete problem-solving situations.
- ILO 6 To be able to solve the problem of sensor integration, and to implement simultaneous localization and mapping in robotics.
- ILO 7 To be able to apply techniques for representing (qualitative) temporal and spatial information in Artificial Intelligence.
Topic 1. Search and planning:
problem-solving as search; adversarial games; classical planning.
Topic 2. Logic and reasoning
review of first-order logic; applications of logic to planning; logic versus reasoning; default `logic'.
Topic 3. AI and probability
review of probability theory; alternative representations of uncertainly; Bayes' networks.
Topic 4. Knowledge representation
ontology-driven database access; formal ontologies and knowledge-representation.
Topic 5. The periphery:
sensors and actuators; sensor integration, simultaneous localization and mapping.
Topic 6. Philosophical issues:
the Turing test; the meaning of `AI'; the problem of consciousness.
Teaching and learning methods
2 hours lectures per week (22 hours in total), 2 hours of lab per fortnight (8 hours in total)
|Written assignment (inc essay)||20%|
Exam and assessments
Lab 1: Syllogisms
Lab 2: Games
Lab 3: Fuzzy logic
Lab 4: SLAM
COMP24011 reading list can be found on the Department of Computer Science website for current students.
Stuart Russell and Peter Norvig: Artificial Intelligence: A Modern Approach, Global Edition, Pearson, 2016
Ronald Brachman: Knowledge representation and reasoning, Morgan Kaufmann, 2004
Simon J. D. Prince: Computer vision : models, learning, and inference, Cambridge University Press, 2012.
|Scheduled activity hours|
|Practical classes & workshops||8|
|Independent study hours|
|Ian Pratt-Hartmann||Unit coordinator|