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Course: CS7032: Agents, AI & Games

Lecturer: Saturnino Luz
Demonstrator: Fasih Haider

Lecture notes and slides

Note: the slides, notes, and practicals (lab sheets) below have been compiled into a single reader, which you may find more convenient. This reader will be updated as the course progresses, so make sure you download it from time to time so that you have the most recent version. Make sure you clean the cache of your web browser when before downloading.

Weekly practicals

  1. Evaluating Evaluative feedback and Describing a Markov Decision Process
  2. TSP path finding with an ACO algorithm
  3. Multi-agent simulation environments
  4. Exploring and formalising Robocode, Robolog ... This week you will install and get acquainted with the Robocode environment. The excercises can be done and handed in during next week's lab.

Course project

This year's project will consist in devising an AI approach to game playing which will be tested in a major Game AI competition. The competition will be chosen based on a survey to be carried out by the class, and presented after the Study Week.


  • TBA


  • General AI and Agents:
    • Russel, S. and P. Norvig, Artificial Intelligence: a Modern Approach. 2nd Edition (preferably). Prentice Hall, 2002.
    • M. Wooldridge, An Introduction to MultiAgent Systems. John Wiley & Sons, 2002. ISBN 0 47149691X.
  • Reinforcement learning (aka neuro-dynamic programming)
    • Bertzekas, D. and Tsitsiklis, J. Neuro-dynamic programming Athena Scientific, 1996.
    • Sutton, R. and A. Barto An Introduction to Reinforcement Learning. MIT Press. 1998.
  • Other references (papers etc):
    • John McCarthy. "Partial formalizations and the Lemmings game" [PDF]
    • Gerhard Weiss. Multiagent Systems. MIT Press, Cambridge, MA, 1999.
      Chapter 1 (link to PDF file; author's website).

Last updated Thu Nov 8 11:46:19 2012 by S Luz «luzs at» by .