CS453 - Introduction to Artificial Intelligence
| TIMETABLE | TEACHING MATERIAL |
| Credits | 20 |
| Level | 4 |
| Semester | Term 1 |
| Availability | Available to participants taking UG Graduate and Degree Apprenticeship programmes, e.g. BSc Hons IT: Software Development. |
| Prerequisites | CS353 Fundamentals of Data Analytics or a good knowledge of Java and/or Python. |
| Learning Activities Breakdown | 12 tutorials, online study and preparation for the coursework and the class test. |
| Assessment | The class is assessed 40% by coursework and 60% by a class test. |
| Lecturer | Leila Shafti |
Aims and Objectives
The aim of the class is to give learners a basic introduction to modern AI. Participants will develop a practical understanding of AI algorithms which enable autonomous systems to make rational decisions, AI systems which encompass a variety of such algorithms to achieve an overall goal, and the implementation of these in a suitable high-level programming language.
Learning Outcomes
After completing this class participants will be able to:
- Understand and define the problem of AI as it relates to autonomous systems.
- Implement key AI algorithms and build AI systems.
- Apply search techniques to enable autonomous systems to choose actions that are appropriate to their goals.
- Apply key techniques to adversarial problems, such as Mini-Max and Monte Carlo Tree search.
- Define problems as planning problems using PDDL and solve them using a planner.
Syllabus
- What is AI? Foundations, history and related disciplines. The state of the art in modern AI including notable applications and successes.
- Intelligent agents: agents and environments, the concept of rationality, the structure of agents, different types of intelligent agent (reactive, deliberative, learning).
- Solving problems by searching: formulating problems as a search for a solution, uninformed search algorithms, informed (heuristic) search algorithms, formulating heuristic functions.
- Beyond classical search: optimisation problems, local search algorithms, genetic algorithms, local search in continuous spaces, searching under non-determinism and partial observability, online search agents and unknown environments.
- Adversarial search: optimal decisions in games, the minimax algorithm, alpha-beta pruning, imperfect real-time decisions, stochastic games, partially observable games, the state of the art in games playing, Monte Carlo Tree Search.
- Classical planning: definition of classical planning, algorithms for planning as state-space search, planning graphs, other approaches., PDDL (Planning Domain Definition Language).
Recommended Reading
This list is indicative only – the class lecturer may recommend alternative reading material. Please do not purchase any of the reading material listed below until you have confirmed with the class lecturer that it will be used for this class.
Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. 4th edition, Pearson, 2020.
Transferable skills
During and after completing this class participants will:
- Develop their generic analysis and problem-solving skills.
Last updated: 2022-12-15 14:06:13