Artificial Intelligence (H) is an introduction to Artificial Intelligence, giving the students an overview of intelligent agent design.

3 hours per week

Artificial Intelligence (M)

None

Examination 75%; Continuous assessment- practical exercises 25%.

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

The coursework cannot be redone because the nature of the coursework is such that it would take a significant number of days to produce it and this effort is infeasible for supporting the re-doing of such coursework over the summer.

The aim of this course is to provide an overview of intelligent agent design, where agents perceive their environment and act rationally to fulfil their goals. Students will gain practical experience in labs, programming various aspects of intelligent systems.

By the end of the course students will be able to:

1. Demonstrate familiarity with the history of AI, philosophical debates, and understand the potential and limitations of the subject in its current form;

2. Explain the basic components of an intelligent agent, and be able to map these onto other advanced subjects such as information retrieval, computer vision, database systems, robotics, human-computer interaction, reactive systems etc

3. Discuss difficulties in computer perception;

4. Discuss basic issues in planning;

5. Explain and apply search-based problem-solving techniques;

6. Formulate and apply Bayesian networks in modelling and planning;

7. Explain and apply utility theory as a probabilistic framework for rational decision making;

8. Explain and apply basic machine learning techniques to learn from rewards and observations.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.