Computational Game Theory

COMP34612 Computational Game Theory 10 Semester 2 Undergraduate Level 3 Xiaojun Zeng Unit coordinator 1 FHEQ level (Framework for Higher Education Qualifications) ' Last part of a Bachelors ' European Credit Transfer & Accumulation System Rating : 5.0 There has been a substantial grow of research activity at the boundaries of game theory, artificial intelligence, economics, computer science, and a number of other disciplines in recent years. The reasons behind this are twofold: On the one hand, game theory and its applications raise many important and challenging computing, learning, and communication problems to CS and AI; On the other hand, game theory provides important insights and powerful frameworks to a number of CS topics, including AI, Multi-agent systems, computer networks as well as many others. The main contents of this module include:1) To introduce the concepts and computational solutions for non-cooperative and cooperative game theory with their applications2) To introduce the machine learning techniques to solve the learning issues arise from the applications of game theory with their applications3) To introduce the mechanism design (the reverse game theory) and its applications for the design of the rules of a game The module includes a major piece of coursework (a group project run over 5 weeks) to apply game theory and learning methods covered to solve the pricing game problem. There has been a substantial grow of research activity at the boundaries of game theory, artificial intelligence, economics, computer science, and a number of other disciplines in recent years. The reasons behind this are twofold: On the one hand, game theory and its applications raise many important and challenging computing, learning, and communication problems to CS and AI; On the other hand, game theory provides important insights and powerful frameworks to a number of CS topics, including AI, Multi-agent systems, computer networks as well as many others. The main contents of this module include:1) To introduce the concepts and computational solutions for non-cooperative and cooperative game theory with their applications2) To introduce the machine learning techniques to solve the learning issues arise from the applications of game theory with their applications3) To introduce the mechanism design (the reverse game theory) and its applications for the design of the rules of a game The module includes a major piece of coursework (a group project run over 5 weeks) to apply game theory and learning methods covered to solve the pricing game problem. This module teaches the fundamental concepts of game theory and their computational methods to enable students to master the concepts/tools from game theory to model/analyse the interaction agents/systems, and to build skills in machine learning and optimisation methods for game analysis and problem solving.    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 COMP24112 Machine Learning Pre-Requisite Optional COMP34111 AI and Games Co-Requisite Optional MATH11121 Mathematical Foundation & Analysis Pre-Requisite Compulsory For Computer Science and Maths students the pre-requisite is MATH11121 or MATH10111. For Single Honours students the pre-requisite is COMP11120 N Demonstration 6 Lectures 12 Practical classes & workshops 10 0 72