AI and Games

COMP34111 AI and Games 10 Semester 1 Undergraduate Level 3 Mingfei Sun Unit coordinator 1 FHEQ level (Framework for Higher Education Qualifications) ' Last part of a Bachelors ' European Credit Transfer & Accumulation System Rating : 5.0 <div style="border:solid windowtext 1.0pt; padding:1.0pt 6.0pt 1.0pt 4.0pt">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.)</div> <div style="border:solid windowtext 1.0pt; padding:1.0pt 6.0pt 1.0pt 4.0pt">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.)</div> <div style="border:solid windowtext 1.0pt; padding:1.0pt 6.0pt 1.0pt 4.0pt">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.</div> 1 Written exam 50% 2 Written assignment (inc essay) 50% COMP11120 Mathematical Techniques for Computer Science Pre-Requisite Compulsory COMP13212 Data Science Pre-Requisite Compulsory MATH11121 Mathematical Foundations & Analysis Pre-Requisite Compulsory MATH10111 Foundations of Pure Mathematics B Pre-Requisite Compulsory COMP34111 COMP11120 (for CS students), COMP13212 or MATH10111 or MATH11121 N Demonstration 3 Lectures 12 Practical classes & workshops 10 Project supervision 5 0 70