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CS255 Artificial Intelligence

CS255 15 CATS (7.5 ECTS) Term 1


Option - CS, CSE, CMS, DM and Data Science

Note: This module is only available to students in the second year of their degree and is not available as an unusual option to students in other years of study.

Academic Aims

This module will introduce the foundational concepts in artificial intelligence and knowledge based systems.

Learning Outcomes

After completing CS255 Artificial Intelligence, a student should be able to:

  • develop an appreciation for Knowledge Based Systems, Intelligent Agents and their architectures.
  • understand a wide variety of knowledge representation and artificial intelligence approaches to planning.
  • understand various methods for search (uninformed and informed) and planning.
  • understand various methods for representing and reasoning under uncertainty.


The module will consist of an introduction to selected topics in Artificial Intelligence. The actual topics taught in any particular year may vary, but will usually be chosen from the following list.

  • Agents: rational agency, logical agents, agent architectures and cooperation.
  • Search: uninformed and informed search, constraint satisfaction problems, games.
  • Planning: partial order planning, conditional planning, monitoring and re-planning.
  • Knowledge Representation: logic, rules, frames, semantic networks, description logic.
  • Bayesian AI: introduction to Bayesian reasoning, directed acyclic graphs and probability, inference in Bayesian networks and decision networks.


  • Russell S and Norvig P, Artificial Intelligence: A Modern Approach, 3rd edition, Prentice-Hall, 2010
  • Brachman R and Levesque H, Knowledge Representation and reasoning, Morgan Kaufmann, 2004
  • Korb K and Nicholson A, Bayesian Artificial Intelligence, Chapman and Hall, 2004


Two-hour examination (80%), coursework (20%)


30 one-hour lectures plus 8 one-hour seminars.