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School of Computer Science and Statistics

Module Descriptor School of Computer Science and Statistics

Module CodeCS3061
Module NameArtificial Intelligence I
Module Short TitleA I
ECTS5
Semester TaughtSemester 2 (Hilary)
Contact Hours33 (22 lecture, 11 tutorial)
Module PersonnelTim Fernando
Learning Outcomes

After successfully completing this module, students should be able to:

  • Describe the basic aims and achievements of artificial intelligence, as well as the challenges facing it
  • Assess notions of computability as they relate to artificial intelligence
  • Reason about the suitability of and trade-offs between basic search strategies
  • Design simple knowledge representation systems for various knowledge-intensive problems
  • Understand the possibilities opened up by meta-interpretation
Learning Aims

An introduction to Artificial Intelligence covering basic topics search and knowledge representation

Module Content

Search, constraint satisfaction, knowledge representation, abduction, meta-interpretation

Recommended Reading List

Russell and Norvig, Artificial Intelligence: A Modern Approach
Poole, Mackworth and Goebel, Computational Intelligence: A logical approach

Module PrerequisitesCS3011
Assessment Details

Written exam (90%) and continuous assessement consisting of lab work and problem sets (10%)
The supplemental assessment will be based solely (i.e. 100%) on the written exam.
Exam duration: 2 hours (annual and supplemental)

Module Website
Academic Year of Data2016-17