CS310 - Foundations of Artificial Intelligence

Aims and Objectives

To help the student to a broad appreciation of the scale and nature of the problems within Artificial Intelligence and to a detailed understanding of some of the fundamental techniques used to address those problems.

Learning Outcomes

After completing this module students will be able to: 

• describe the operation of several foundational AI techniques, such as search, planning, machine learning, and constraint management 
   
• assess the most suitable AI method to apply to different problems within the field 
   
• implement AI programs that can be applied to solve real world scenarios such as game playing, or object classification 
   
• debate the social and environmental impacts of state-of-the-art AI techniques

Syllabus

The class will make partial use of the 4th edition of AI: A Modern Approach by Russell and Norvig. The topics to be covered will include:

1. Introduction: What is AI? Foundations, history and related disciplines. The state of the art in modern AI including notable applications and successes.
2. Intelligent agents: agents and environments, the concept of rationality, the structure of agents, different types of intelligent agent (reactive, deliberative, learning).
3. Solving problems by searching: formulating problems as a search for a solution, uninformed search algorithms, informed (heuristic) search algorithms, formulating heuristic functions.
4. 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.
5. Constraint satisfaction problems: defining CSPs, constraint propagation, backtracking search in CSPs, local search in CSPs, exploiting problem structure.
6. Classical planning: definition of classical planning, algorithms for planning as state-space search, planning graphs, other approaches.
7. Machine learning principles, including the perceptron, linear and non-linear problems, and activations.
8. Neural networks, including multi layer perceptrons, recurrent networks, and convolutional neural networks.
9. An introduction to deep learning, and the associated social, ethical, and environmental implications.

The class will focus on the practicalities of using these techniques to build intelligent agents and solve problems. Practical work will be set, including the programming of an intelligent agent for a specified task.

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.

Artificial Intelligence: A Modern Approach, 4th Edition. Stuart Russell and Peter Norvig Prentice Hall.

Last updated: 2025-08-19 11:28:35