Durham University
Programme and Module Handbook

Undergraduate Programme and Module Handbook 2005-2006 (archived)

Module COMP3352: ADVANCED ARTIFICIAL INTELLIGENCE (40 CREDITS)

Department: COMPUTER SCIENCE

COMP3352: ADVANCED ARTIFICIAL INTELLIGENCE (40 CREDITS)

Type Open Level 3 Credits 40 Availability Available in 2005/06 Module Cap None. Location Durham

Prerequisites

  • Software Applications (COMP2071) AND Programming and Reasoning (COMP2171).

Corequisites

  • None.

Excluded Combination of Modules

  • Advanced Artificial Intelligence (20 Credits) COMP3311.

Aims

  • To give students a familiarisation with and an understanding of a range of subjects within the broad context of Artificial Intelligence.

Content

  • Multi-agent systems: design of intelligent agents.
  • framework for programming agents.
  • multi-agent interactions.
  • applications of agent-based systems.
  • Advanced information systems: data modelling.
  • web data management.
  • Semantic and Knowledge management.
  • web semantic/ontology languages.
  • Advanced topics in AI: Natural language processing.
  • learning.
  • reasoning under uncertainty.
  • planning.
  • the associated problem-solving paradigms and knowledge representation schemes.
  • Mechanised Reasoning: Introduction to mechanised reasoning.
  • the logical bases of reasoning methods.
  • automated and interactive reasoning.
  • systems and applications of mechanised reasoning.

Learning Outcomes

Subject-specific Knowledge:
  • Students should be able to: Understand how Artificial Intelligence methods impact on the different problem areas in four topic areas.
  • Have knowledge of different paradigms and techniques relevant to each of the four topics.
  • Understand how Artificial Intelligence is relevant to new technology and technological developments in each of the four topics.
Subject-specific Skills:
  • Demonstrate, for each of the four topics, that they have conducted research and self-study to further their knowledge beyond the taught materials.
Key Skills:

    Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

    • Lecturing demonstrates what is required to be learned and the application of the theory to practical examples.
    • Homework problems identify areas where further independent research should be conducted.
    • Summative examinations the knowledge acquired and the students' ability to use this knowledge to solve complex problems.

    Teaching Methods and Learning Hours

    Activity Number Frequency Duration Total/Hours
    Lectures 38 2 per week 2 hours 76
    Preparation and Reading 324
    Total 400

    Summative Assessment

    Component: Coursework Component Weighting: 30%
    Element Length / duration Element Weighting Resit Opportunity
    coursework 100%
    Component: Examination Component Weighting: 70%
    Element Length / duration Element Weighting Resit Opportunity
    three-hour examination 100%

    Formative Assessment:

    Example exercises given through the course.

    ■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University