AI and Games

COMP34111 AI and Games 10 Semester 1 Undergraduate Level 3 Jonathan Shapiro Unit coordinator Department of Computer Science 1 FHEQ level (Framework for Higher Education Qualifications) ' Last part of a Bachelors ' European Credit Transfer & Accumulation System Rating : 5.0 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 Exploration & Exploitation, learning through "self-play", Monte Carlo Tree Search and AlphaGo.)  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 Exploration & Exploitation, learning through "self-play", Monte Carlo Tree Search and AlphaGo.)  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.  ILO 1: Analyze what it means to play a game well by formulating best-response strategies, identifying equilibrium points, and critically evaluating the assumptions and limitations of these solution concepts, including alternative approaches.ILO 2: Explain the concept of a game by accurately defining its core components, including players, pay-off functions, and rules, and distinguish between normal-form and extensive-form representations.ILO 3: Explain and assess learning-based approaches to game playing by describing the exploration–exploitation trade-off, explaining learning through self-play, and analyzing how Monte Carlo Tree Search and modern game-playing systems achieve strong performance.ILO 4: Apply and compare algorithmic methods for finding good game plans by describing the computational complexity of equilibrium computation, implementing or tracing the minimax algorithm with alpha–beta pruning, and explaining the roles of evaluation functions and search procedures in game-playing programs.ILO 5: Define and interpret strategies as complete plans for decision-making in games, and represent strategies appropriately for different game structures. 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 COMP11120 (for CS students) and COMP13212 are pre-requisites. MATH11121 or equivalent (for CM students).COMP11120 (for CS students) and COMP13212 are pre-requisites. MATH11121 or equivalent (for CM students). N Demonstration 3 Lectures 12 Practical classes & workshops 10 Project supervision 5 0 70