Symbolic Artificial Intelligence
| Code | School | Level | Credits | Semesters |
| COMP3070 | Computer Science | 3 | 20 | Autumn Malaysia |
- Code
- COMP3070
- School
- Computer Science
- Level
- 3
- Credits
- 20
- Semesters
- Autumn Malaysia
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’ll cover include: first order logic; resolution; description logic; default reasoning; rule-based systems; belief networks; planning and reasoning about actions. You’ll have two hours of lectures and one hour of labs each week for this course.
Target Students
Available to Level 3 and Level 4 students in the School of Computer Science. Available to inter-campus mobility students and other exchange students in computer science. This module is not available to students not listed above, without explicit approval from the module convenor(s). Prior knowledge of algorithms and complexity, propositional logic, set theory, programming skills is required. This module is part of the Artificial Intelligence, Modelling and Optimisation theme in the School of Computer Science.
Classes
- One 2-hour lecture each week for 11 weeks
- One 2-hour laboratory each week for 11 weeks
Assessment
- 20% Coursework 1: Individual coursework (20%) involving Python Programming and report write-up
- 30% Coursework 2: Individual coursework (30%) involving Python Programming and report write-up
- 50% In Class Tests: Continuous assessment (35%) and a lab test (15%)
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.
Conveners
- Doreen Ying Ying Sim