Catalogue of Modules, University of Nottingham

G53KRR Knowledge Representation and Reasoning
(Last Updated:03 May 2017)

Year  17/18

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.

Taught Semesters:

SemesterAssessment
Autumn Assessed by end of Autumn Semester 

Prerequisites: or equivalent knowledge of propositional and first-order predicate logic.

MnemTitle
G51MCS Mathematics for Computer Scientists 

Corequisites:  None.

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:
   
  • Reading List
  • Method and Frequency of Class:

    ActivityNumber Of WeeksNumber of sessionsDuration of a session
    Lecture 11 weeks2 per week1 hour

    Activities may take place every teaching week of the Semester or only in specified weeks. It is usually specified above if an activity only takes place in some weeks of a Semester

    Method of Assessment: 

    Assessment TypeWeightRequirements
    Exam 1 100 2 hr written examination 

    Convenor: 
    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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