COMP24412 Knowledge Based AI syllabus 2021-2022

COMP24412 Knowledge Based AI

Level 2
Credits: 10
Enrolled students: pending

Course leader: Viktor Schlegel

Additional staff: view all staff

Requisites

• 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

Semester	Event	Location	Day	Time	Group
Sem 2 w20-27,31-34	Lecture	MEC Hall 2.013 M&T	Thu	11:00 - 12:00	-
Sem 2 w20-27,31-34	Lecture	MEC Hall 2.013 M&T	Tue	13:00 - 14:00	-
Sem 2 w22,24,26,31,33-34	Lab	MEC Hall 1.016 Computer Cluster	Mon	11:00 - 13:00	-
Sem 2 w22,24,26,31,33-34	Lab	MEC Hall 1.016 Computer Cluster	Thu	14:00 - 16:00	-

• 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

 

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

• 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