Artificial Intelligence 4 - homepage

This page will hold day-to-day information about the AI4 module, taught primarily by Roderick Murray-Smith (RMS)

The moodle page for this course is but it will be less functional than this page


Hand out October 19th, hand-in November 25th (9am). You should hand in your printed report to the appropriate box. You should also use e-mail to e-mail the python source code for your solutions to Use the subject AI4-AE in your e-mail.
This assignment will have three parts. The practical parts are based on the Berkeley Pac-Man exercises, which should give you experience at implementing a number of the topics you have learned about so far. The third part gives you some freedom to develop in your own direction, and push your programming and AI skills as far as you can. As mentioned in the lecture, ou can use the report to discuss the rationale behind your implementation of the first two parts of the project.
1. Complete the Search in Pac-Man assignment You will fill in portions of and during the assignment. You should submit these two files along with your report. (30%)

clip_image004.jpg 2. Complete the Multiagent Pac-Man project You will fill in portions of during the assignment. You should submit this file with your code and comments. You may also submit supporting files (like, etc.) that you use in your code. (30%)

3. Use of evolutionary approaches to improve the performance of agents in the Pac-Man environment. Use these, in any way you see fit, with the Pac-man environment resources (this can involve techniques from some of the more advanced labs on the Pac-Man site). Describe how you used these methods, provide code, and results of simulations and document these clearly in your report. Include a 4-page literature review on the use of artificial intelligence in the games and entertainment industry, discussing techniques used, citing academic literature. (40%)

Marking scheme

The marking scheme for parts 1 and 2 will follow the credit marks specified in the Berkeley assignments, and will sum to 60% of the assignment.
The report on the use of evolutionary approaches in game agents is worth 60% of the assignment total. The report should be no more than 10 pages of text. An `A' grade report would be well written, citing appropriate literature, show creative use of the AI techniques to solve important and interesting problems in game agent design, and take account of practical constraints. A `B' grade might be due to poorer writing, less insightful use of algorithms, poorer presentation of the simulation results. A `C' grade would have some basic use of evolutionary techniques, but would have shortcomings in the implementation, testing, presentation or justification of choices. A `D' grade report would have the most basic use of evolutionary techniques in the multiagent example, but they would not be well justified, and might not be desirable.

Lecture times

Note PDFs of lectures can only be downloaded from University machines (or while VPNed in).
Lecture Topic Readings Exercises/Labwork/Handouts/WWW Links
1, 27th Sept Introduction


2, 28th Sept

Intelligent Agents

Ch2 Q2.5,2.6
3, 29th Sept

Uninformed search & representation

Ch3 Q3.2,3.7,3.13
4th Oct

Social Signal Processing
Understanding Social Interactions through
Nonverbal Communication Analysis

  video (computer frustration)
6th Oct Tutorial: discussion of DARPA robotics grand challenges.
4, 11th Oct

Informed search, optimisation and evolutionary approaches

Optimisation, design and evolutionary approaches

Ch4 Q4.2,4.3


5. 12th Oct


Game playing

6. 13th Oct Uncertainty
Ch 13


7. 18th Oct

Decision making under uncertainty

Ch 16
  • Q16.2,16.11
  • Try Qs 16.1, 16.4 with classmates
8. 19th Oct

Intro to Belief Nets & Expert systems issues

Ch 14.1-14.3,14.7 (no detailed questions on inference algorithms)

20th Oct

Getting going with the Berkeley Pac-Man environment (in Boyd Orr lab)

    Complete the Search in Pac-Man assignment
9. 25th Oct



10. 26th Oct Perception
Read Ch24, focus on Ch24.1,24.6,24.7

Sine-wave speech demo
McGurk effect

Gregory face videos

Support for assessed exercise

11. 1st Nov

Machine Learning

Ch 18.1-18.3,18.6 Q18.2,18.3,18.4
12. 2nd Nov Machine Learning, Self-organisation.
12. 8th Nov

Neural networks




13. 9th & 15th Nov

Neural networks, cont




15. 16th & 22nd Nov

Affective Computing



17th Nov

NN lab


Example MATLAB programs.
Run mlpdemo, SOMdemo, SOM3Ddemo within MATLAB, explore the behaviour with your own classification challenges, and try changing the number of hidden units in the model, or adjust the regression problem and noise levels).

Old Lab on neural nets

23rd Nov

No lecture - free for work on assessed exercise

17. 29th Nov

Ethical Issues in AI

18. 30th Nov

Philosophical Issues

Ch26,27 Q26.1,26.7
  • Computing Machinery and Intelligence, A. M. Turing
  • Turing Page
  • Turing Archive
  • Course Textbook


    Artificial Intelligence: A Modern Approach, 3rd Edition,
    by Stuart Russell and Peter Norvig.
    ISBN: 978-0132071482


    Required text - contents of chapters specified in lectures will be examinable.

    Book home page -

    Some Other Resources

    Other points