Module: Advanced Algorithms & A.I. Search

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Advanced Algorithms & A.I. Search
Module Code	CSC1047 (ITS: CA318)
Faculty	Engineering & Computing	School	Computing
NFQ level	8	Credit Rating	5

Description

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

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2. identify what type of problems are suitable for an AI approach (search, learning, self-modification of some sort).
5. 1
6. 1D763CD1-AE05-0001-F469-552021E01B96
7. evaluate the scale / dimensionality / searchspace size of such problems
10. 2
11. 1D763CD1-B3AC-0001-E192-E5006F103840
12. define the search space in such problems
15. 3
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17. evaluate which AI methods are suitable for a given problem
20. 4
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22. 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)
25. 5
26. 1D763CD1-E0F6-0001-2147-15E01D001FDF
27. apply some of the techniques covered in the course (search, learning, evolution)
30. 6
31. 1D763CD1-EB72-0001-DB46-1C7088A02100
32. code the techniques in the course in the student's preferred programming language
35. 7
36. 1D763CD1-F35B-0001-4282-1BDC1DF910B8
37. test the techniques in an Internet-based system with a massive search space and multiple conflicting goals
40. 8

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Laboratory	12	No Description
Lecture	24	No Description
Independent Study	89	No Description
Total Workload: 125

Section Breakdown
CRN	10227	Part of Term	Semester 1
Coursework	40%	Examination Weight	60%
Grade Scale	40PASS	Pass Both Elements	N
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Mark Humphrys	Module Teacher	Hemant Kumar Singh

Type	Description	% of total	Assessment Date
Assessment Breakdown
Assignment	Internet based programming project to solve AI problem. CA will be unique to student (or student pair) and need to be written up in a unique document. In cases where a pair or group project is allowed: Students will be asked to identify individual contributions to the project. They will normally get the same mark, but in cases of clearly lopsided contributions they may receive different marks.	40%	Once per semester
Formal Examination	End-of-Semester Final Examination	60%	End-of-Semester

Reassessment Requirement Type
Resit arrangements are explained by the following categories; RC1: A resit is available for both^* components of the module. RC2: No resit is available for a 100% coursework module. RC3: No resit is available for the coursework component where there is a coursework and summative examination element. ^* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment

Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

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).

Indicative Reading List

Books:

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

Articles:
None

Other Resources

None