Artificial Intelligence for Decision Making

Module code: ECS535U

Credits: 15.0
Semester: SEM2

Contact: Dr Yongxin Yang

This module covers a range of Artificial Intelligence techniques employed for decision making. During this module, students will learn the theoretical fundamentals and the practical skills required to implement autonomous agents with their own decision-making mechanisms. The syllabus of this module includes the following topics:

Basics of Search, Tree Search and Monte Carlo Search.
Monte Carlo Tree Search
Evolutionary Algorithms: Rolling Horizon Evolution
Game Theory, Minimax, Alpha Beta Search and Opponent Modelling.
Reinforcement learning: markov decisions processes, value and action-value functions, optimal policies.
Authored Decision-Making algorithms: Finite State Machines, Behaviour Trees, Goal-Oriented Action Planning
Steering Behaviours
Multi-Agent Systems

Connected course(s): UDF DATA
Assessment: 100.0% Coursework
Level: 5