Artificial Intelligence (3 Years) [BSc]

Symbolic AI


Unit code: COMP24412
Credit Rating: 10
Unit level: Level 2
Teaching period(s): Semester 2
Offered by School of Computer Science
Available as a free choice unit?: Y

Requisites

None

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.

Aims

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

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.

Teaching and learning methods

Lectures

22 in total, 2 per week

Laboratories

10 hours in total, 5 2-hour sessions.

Learning outcomes

Learning outcomes are detailed on the COMP24412 course unit syllabus page on the School of Computer Science's website for current students.

Employability skills

  • Analytical skills
  • Problem solving

Assessment methods

  • Written exam - 70%
  • Written assignment (inc essay) - 5%
  • Practical skills assessment - 25%

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

Recommended reading

COMP24412 reading list can be found on the School of Computer Science website for current students.

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
  • Independent study hours - 64 hours

Teaching staff

Giles Reger - Unit coordinator

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