Symbolic Artificial Intelligence
| Code | School | Level | Credits | Semesters |
| COMP3008 | Computer Science | 3 | 20 | Autumn UK |
- Code
- COMP3008
- School
- Computer Science
- Level
- 3
- Credits
- 20
- Semesters
- Autumn UK
Summary
This course examines how knowledge can be represented symbolically and how it can be manipulated in an automated way by reasoning programs. Some of the topics you will cover include: propositional and first order logics; resolution; SAT; constraint satisfaction; description logics and non-monotonic reasoning. You will have two hours of lectures and two hours of labs each week for this course. You will get hands-on experience of formulating real-world problems and using state-of-the-art reasoning software packages to solve them.
Target Students
Available to Level 3 and Level 4 students in the School of Computer Science. Prior knowledge of algorithms and complexity, propositional logic, set theory and programming skills is required. This module is part of the Artificial Intelligence, Modelling and Optimisation theme in the School of Computer Science.
Assessment
- 50% Coursework 1: Continuous assessment and individual projects.
- 50% Exam 1 (2-hour): ExamSys (in person). Requirements: Closed book examination. Reassessment 100% examination.
Assessed by end of autumn semester
Educational Aims
To convey an understanding of the issues involved in representing knowledge in a form understandable by a computer and using automated reasoning to answer queries about the knowledge.Learning Outcomes
Knowledge and Understanding:
- Knowledge of common knowledge representation formalisms and their properties.
- Understanding of common reasoning mechanisms.
- Knowledge of common automated reasoning systems.
Intellectual Skills:
- Ability to apply logical tools to perform reasoning.
- Ability to interpret the results of reasoning.
Professional Skills:
- Ability to choose an appropriate knowledge representation language for a given problem.
- Ability to formulate knowledge using a common knowledge representation language.
- Ability to apply off-the-shelf solvers to perform reasoning.
- Ability to analyse the performance of a solver.
Transferable Skills:
- Ability to formalise real-world problems.
- Ability to formulate real-world problems to enable automated reasoning.