Total Credits: 10
Level: Level 3
Target Students: Part II undergraduate students in the School of Computer Science. Also available to students from other Schools with the agreement of the module convenor.This module is part of the AI, Modelling and Optimisation theme in the School of Computer Science. Available to JYA/Erasmus students.
|Autumn||Assessed by end of Autumn Semester|
Prerequisites: or equivalent knowledge of propositional and first-order predicate logic.
|G51MCS||Mathematics for Computer Scientists|
Summary of Content: This module examines how knowledge can be represented symbolically and how it can be manipulated in an automated way by reasoning programs. Some of the topics you’ll cover include: first order logic; resolution; description logic; default reasoning; rule-based systems; belief networks. You’ll have two hours of lectures each week for this module.
Module Web Links:
Method and Frequency of Class:
|Activity||Number Of Weeks||Number of sessions||Duration of a session|
|Lecture||11 weeks||2 per week||1 hour|
Method of Assessment:
|Exam 1||100||2 hr written examination|
Dr N Alechina
Education Aims: To convey an understanding of the issues involved in representing knowledge in a form understandable by a computer and using automated reasoning to answer queries about the knowledge.
Learning Outcomes: Knowledge and Understanding: Knowledge of common knowledge representation formalisms and reasoning mechanisms. Knowledge of common ontology languages.Intellectual Skills: Ability to represent knowledge in a knowledge representation language. Ability to derive new facts using resolution, forward and backward rule chaining, and other common inference mechanisms. Professional Skills: Ability to design a simple ontology or an expert system and choose the right tools for implementing the query answering/reasoning mechanism for it. Transferable Skills: To be able to solve problems using formal representation of knowledge and reasoning techniques.
Offering School: Computer Science
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