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COMP24412 Knowledge Based AI syllabus 2020-2021

COMP24412 materials

COMP24412 Knowledge Based AI

Level 2
Credits: 10
Enrolled students: 81

Course leader: Giles Reger


Additional staff: view all staff

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.

Assessment methods

  • 30% Written exam
  • 5% Coursework
  • 65% Practical skills assessment
Timetable
SemesterEventLocationDayTimeGroup
Sem 2 w20-26,29-32 Lecture Mon 10:00 - 11:00 -
Sem 2 w20-26,29-32 Lecture Wed 11:00 - 12:00 -
Sem 2 w22,24,26,30,32 ONLINE LabORATORY Fri 15:00 - 17:00 G
Sem 2 w22,24,26,30,32 ONLINE LabORATORY Mon 16:00 - 18:00 F
Themes to which this unit belongs
  • Natural Language, Representation and Reasoning

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.
 
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.

Aims

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

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 methods

Lectures

22 in total, 2 per week

Laboratories

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 (24 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, 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 

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.