
Apply through UCAS
- UCAS course code
- I100
- UCAS institution code
- M20
MEng Computer Science with Industrial Experience / Course details
Year of entry: 2021
- View tabs
- View full page
Course unit details:
Knowledge Based AI
Unit code | COMP24412 |
---|---|
Credit rating | 10 |
Unit level | Level 2 |
Teaching period(s) | Semester 2 |
Offered by | Department of Computer Science |
Available as a free choice unit? | Yes |
Overview
Intelligent systems need to be able to represent and reason about the world. This course provides an introduction to the key ideas in knowledge representation and different types of automated reasoning. The course is a mixture of theoretical and practical work: at the end of the course students will know the principles that such systems use, and they will have experience of implementing those principles in running systems.
Pre/co-requisites
Unit title | Unit code | Requirement type | Description |
---|---|---|---|
Introduction to AI | COMP24011 | Co-Requisite | Compulsory |
Students who are not from the School of Computer Science must have permission from both Computer Science and their home School to enrol.
COMP24011 is a co-requisite of this course
Aims
The aim of this course is to provide the conceptual and practical (systems building) foundations for knowledge representation and reasoning in Artificial Intelligence.
Learning outcomes
- ILO 1 Describe, apply and demonstrate syntactic and semantic formal models for natural language processing
- ILO 2 Describe, differentiate and apply different knowledge representation formalisms for modelling knowledge bases
- ILO 3 Explain how these KR formalisms affect the reasoning process
- ILO 4 Apply and demonstrate knowledge-based learning methods
- ILO 5 Write Prolog programs to solve automated reasoning tasks and explain how they will execute
- ILO 6 Describe the syntax and semantics of first-order logic and use it to model problems
- ILO 7 Apply reasoning techniques (transformation to clausal form, resolution, saturation) to establish properties of first-order problems
- ILO 8 Explain the theoretical limitations of first-order logic and the associated reasoning methods
Syllabus
First-Order Logic and Automated Reasoning
Syntax and Semantics
Translation to clausal form
Ordered Resolution
Saturation based proof search
Model Construction
Prolog
Syntax and execution
Simple logical programs
Relation to backward chaining with Horn clauses
Theorem Proving with Prolog
Knowledge Representation
Ontological Engineering
Categories and Objects
Events
Reasoning Systems for Categories
Semantic networks
Description logics
Reasoning with Default Information
Knowledge in Learning
A Logical Formulation of Learning
Inductive Logic Programming
Knowledge in Learning
Explanation-Based Learning
Learning Using Relevance Information
Natural Language Semantics
Interfacing with Natural Language Processing
Grammar & parsing
Montague Semantics
Semantic Parsing
Natural Logic Inference
Teaching and learning methods
Lectures
22 in total, 2 per week
Laboratories
10 hours in total, 5 2-hour sessions.
Employability skills
- Analytical skills
- Problem solving
Assessment methods
Method | Weight |
---|---|
Written exam | 30% |
Written assignment (inc essay) | 5% |
Practical skills assessment | 65% |
Feedback methods
The course has a number of lab exercises which are marked in the lab as usual, and feedback on these exercises is provided by written comments on the work and orally by the marker.
Recommended reading
COMP24412 reading list can be found on the Department of Computer Science website for current students.
Study hours
Scheduled activity hours | |
---|---|
Assessment written exam | 2 |
Lectures | 24 |
Practical classes & workshops | 10 |
Independent study hours | |
---|---|
Independent study | 64 |
Teaching staff
Staff member | Role |
---|---|
Giles Reger | Unit coordinator |
Additional notes
Course unit materials
Links to course unit teaching materials can be found on the School of Computer Science website for current students.