Catalogue of Modules, University of Nottingham

G53DIA Designing Intelligent Agents
(Last Updated:03 May 2017)

Year  16/17

Total Credits: 20

Level: Level 3

Target Students:  Part II UG students in the School of CS. Also available to MSc students in the School of CS, and 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 CS.   Available to JYA/Erasmus students.

Taught Semesters:

Spring Assessed by end of Spring Semester 

Prerequisites: Or G51IAI (2014-15)

G51FAI Fundamentals of Artificial Intelligence 

Corequisites:  None.

Summary of Content:  
You’ll be given a basic introduction to the analysis and design of intelligent agents, software systems which perceive their environment and act in that environment in pursuit of their goals. Spending around four hours each week in lectures and tutorials, you’ll cover topics including task environments, reactive, deliberative and hybrid architectures for individual agents, and architectures and coordination mechanisms for multi-agent systems. Module Web Links:
  • Reading List
  • Method and Frequency of Class:

    ActivityNumber Of WeeksNumber of sessionsDuration of a session
    Lecture 8 weeks2 per week2 hours

    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
    Coursework 1 50 2500 word report and code of an agent program 
    Coursework 2 50 2500 word report and code of an agent program 

    Dr B Logan

    Education Aims:  
    To develop a basic understanding of the problems and techniques of building intelligent agents
    To give an appreciation of the trade-offs inherent in the design of agent-based systems,
    To illustrate these through a project involving the construction of a simple agent-based system
    To develop new analysis and design skills appropriate to more complex AI problems.

    Learning Outcomes:  Knowledge and Understanding
    Understanding of the problems and techniques in the design of intelligent agents, knowledge of common agent architectures.

    Intellectual Skills
    The ability to understand and logically evaluate agents' requirements and specifications.
    The ability to analyse agent behaviour in a variety of environments.

    Professional Skills
    Enhanced AI programming skills.

    Transferable Skills
    Enhanced systems analysis and design skills.

    Offering School:  Computer Science

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