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This is an archived syllabus from 2021-2022

COMP24412 Knowledge Based AI syllabus 2021-2022

COMP24412 materials

COMP24412 Knowledge Based AI

Level 2
Credits: 10
Enrolled students: 125

Course leader: Viktor Schlegel

Additional staff: view all staff


  • Co-Requisite (Compulsory): COMP24011

Additional requirements

  • 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

Assessment methods

  • 30% Written exam
  • 70% Practical skills assessment
Sem 2 w20-27,31-34 Lecture 1.1 Tue 13:00 - 14:00 -
Sem 2 w22,24,26,31,33-34 Lab G23 Mon 11:00 - 13:00 -
Sem 2 w22,24,26,31,33-34 Lab G23 Thu 14:00 - 16:00 -
Themes to which this unit belongs
  • Natural Language, Representation and Reasoning


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.
This course unit detail provides the framework for delivery in 20/21 and may be subject to change due to any additional Covid-19 impact.  Please see Blackboard / course unit related emails for any further updates.


The aim of this course is to provide the conceptual and practical (systems building) foundations for knowledge representation and reasoning in Artificial Intelligence.


First-Order Logic and Automated Reasoning
Syntax and Semantics
Translation to clausal form
Ordered Resolution
Saturation based proof search
Model Construction
Syntax and execution
Simple logical programs
Relation to backward chaining with Horn clauses
Theorem Proving with Prolog
Knowledge Representation
Ontological Engineering
Categories and Objects
Reasoning Systems for Categories
Semantic networks
Description logics
Reasoning with Default Information
Inductive and Abductive Reasoning
Learning as a Logic Problem
Learning with Knowledge
Inductive Logic Programming
Explanations and Diagnosis
Explanation-Based Learning

Teaching methods

Synchronous Sessions

11, 1 x per week

Lecture Video material

11 hr


10 hours in total, 5 2-hour sessions.

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.

Study hours

  • Assessment written exam (2 hours)
  • Lectures (22 hours)
  • Practical classes & workshops (10 hours)

Employability skills

  • Analytical skills
  • Problem solving

Learning outcomes

On successful completion of this unit, a student will be able to:

  • ILO 1 Describe, differentiate and apply different knowledge representation formalisms for modelling knowledge bases
  • ILO 2 Describe the syntax and semantics of first-order logic (and the Datalog and Prolog fragments) and use it to model problems
  • ILO 3 Demonstrate the forward and backward chaining reasoning methods and compare their implementation and practical characteristics (e.g. efficiency, termination)
  • ILO 4 Apply resolution-based reasoning techniques (transformation to clausal form, resolution, saturation) to establish properties of first-order problems
  • ILO 5 Explain the theoretical limitations of reasoning techniques for (fragments and extensions of) first-order logic
  • ILO 6 Write Prolog programs to solve automated reasoning tasks and explain how they will execute
  • ILO 7 Differentiate between deductive, inductive and abductive reasoning and apply them to perform learning and inference in knowledge based systems
  • ILO 8 Relate knowledge based approaches to real world applications such as (but not limited to) program synthesis or circuit design verification

Reading list

No reading list found for COMP24412.

Additional notes

Course unit materials

Links to course unit teaching materials can be found on the School of Computer Science website for current students.