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Module Specifications

Archived Version 2014 - 2015

Module Title Advanced Algorithms and A.I. Search
Module Code CA318
School School of Computing

Online Module Resources

Module Co-ordinatorDr Mark HumphrysOffice NumberL2.25
NFQ level 8 Credit Rating 5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None

To introduce students to Search-based and other advanced algorithms. To introduce one definition of Artificial Intelligence (AI) as algorithms that search for solutions in a search-space. To address what defines AI as distinct from other parts of Computer Science, and some basic algorithms in search, learning and evolution. To implement these ideas in a competitive internet-based project.

Learning Outcomes

1. identify what type of problems are suitable for an AI approach (search, learning, self-modification of some sort).
2. evaluate the scale / dimensionality / searchspace size of such problems
3. define the search space in such problems
4. evaluate which AI methods are suitable for a given problem
5. logically estimate the division of labour needed between human effort (searchspace definition, pruning, heuristic design) and machine effort (how much of the space the machine will search in the time available)
6. apply some of the techniques covered in the course (search, learning, evolution)
7. code the techniques in the course in the student's preferred programming language
8. test the techniques in an Internet-based system with a massive search space and multiple conflicting goals

Workload Full-time hours per semester
Type Hours Description
Laboratory12No Description
Lecture24No Description
Independent Study89No Description
Total Workload: 125

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Philosophy of AI.

History of AI.

Machine Learning.

Neural Networks, Back-propagation.

Other forms of learning.

Machine Evolution.

Genetic Algorithms.

Other forms of evolution.

Summary - Solution spaces, heuristic search, learning and evolution.

Architectures of autonomous agents.

The World-Wide-Mind (Project).

Assessment Breakdown
Continuous Assessment25% Examination Weight75%
Course Work Breakdown
TypeDescription% of totalAssessment Date
Reassessment Requirement
Resit arrangements are explained by the following categories;
1 = A resit is available for all components of the module
2 = No resit is available for 100% continuous assessment module
3 = No resit is available for the continuous assessment component
Indicative Reading List

  • George F. Luger: 0, Artificial Intelligence, 5th onwards,
Other Resources

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