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COMP34111 AI and Games syllabus 2021-2022

COMP34111 AI and Games

Level 3
Credits: 10
Enrolled students: 150

Course leader: Jonathan Shapiro


Additional staff: view all staff

Requisites

  • Pre-Requisite (Compulsory): COMP11120
  • Pre-Requisite (Compulsory): COMP13212

Additional requirements

  • For students who did not take COMP13212 the pre-requisite is COMP14112

Assessment methods

  • 50% Written exam
  • 50% Coursework
Timetable
SemesterEventLocationDayTimeGroup
Sem 1 w1-5,7 Lecture Stopford TH 2 Thu 11:00 - 12:00 -
Sem 1 w1-5,7 Lecture Simon TH A Fri 13:00 - 14:00 -
Sem 1 w2,4,7 ONLINE Examples Thu 17:00 - 18:00 -
Sem 1 w8-12 ONLINE LabORATORY Fri 12:00 - 14:00 -

Overview

The main contents of this module include:

 

1. What is a game? (Definition of game, pay-off function, representations in normal form, and extensive form.)

2. What is a plan for decision-making in a game context? (Definition of strategy, representations of strategy.)

3. What does it mean to play a game well? (Definition of best-response strategy, equilibrium point, discussion of the validity of these concepts, discussion of alternatives.)

4. Properties of the Nash equilibrium. (How it incentivizes bad outcomes to prevent opponents from taking advantage.)

5. How do we find good game plans? (Complexity of finding equilibrium points, minimax algorithm, alpha-beta pruning, discussion of the components of a typical game playing program via evaluation function and alpha-beta search)

6. How do we learn good game plans? (Introduction to reinforcement learning, learning through "self-play", TD-learning, Monte Carlo Tree Search.)

Aims

The aim of the course is to introduce students to the main concepts of non-cooperative game theory and the game solution concept of the Nash equilibrium. Different categories of games and different approaches to effective play in games is developed. During the first six weeks of the course, conceptual and theoretical material is developed. During the final 5 weeks, the students put this material into practice by developing an AI agent which plays a particular game.

Study hours

  • Demonstration (3 hours)
  • Lectures (12 hours)
  • Practical classes & workshops (10 hours)
  • Project supervision (5 hours)

Reading list

No reading list found for COMP34111.