Module Title:   AI for Games

Module Credit:   20

Module Code:   CM-0328D

Academic Year:   2015/6

Teaching Period:   Semester 2

Module Occurrence:   A

Module Level:   FHEQ Level 6

Module Type:   Standard module

Provider:   Computer Science

Related Department/Subject Area:   SCIM (Dept of Computer Science)

Principal Co-ordinator:   Dr P Trundle

Additional Tutor(s):   tbc

Prerequisite(s):   None

Corequisite(s):   None

Aims:
To provide knowledge and experience of the creation of artificial opponents for a variety of games, using Artificial Intelligence techniques.

Learning Teaching & Assessment Strategy:
The delivery of the module will consist of lectures and lab classes supplemented by students own lab work and directed study. The coursework will involve students in the design of a AI game playing opponent. Supplementary assessment is by examination, based on the original assignment.

Lectures:   24.00          Directed Study:   152.00           
Seminars/Tutorials:   0.00          Other:   0.00           
Laboratory/Practical:   24.00          Formal Exams:   0.00          Total:   200.00

On successful completion of this module you will be able to...

Critically review a range of AI approaches and their application to designing computerised game opponents. Demonstrate critical understanding of game strategies and of the psychology of human game players.

On successful completion of this module you will be able to...

Design computer game opponents. Analyse, design and implement AI software applicable to games, and also to finance and decision support.

On successful completion of this module you will be able to...

Seek information from appropriate sources. Demonstrate problem solving techniques and preparation for further research in the area.

  Coursework   40%
 
  A coursework involving the creation of an AI opponent
  Coursework   60%
 
  A coursework involving the creation of an AI opponent
  Examination - closed book   100%
 
  Supplementary closed book examination

Outline Syllabus:
Games of complete information, search trees, evaluation functions, examples: chess, draughts, go, etc, application to a particular game.

Games of incomplete information: opponent modelling, probabilistic ideas, applications: Poker, Magic - The Gathering, etc.

Real-time games: path finding, terrain analysis, machine learning approaches, rule-based approaches, making artificial people, application to a particular real time multiplayer game environment.

The computer games industry.

Version No:  6