Module: Advanced Algorithms and A.I. Search

Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title	Advanced Algorithms and A.I. Search
Module Code	CA318
School	School of Computing
Module Co-ordinator	Semester 1: Mark Humphrys Semester 2: Mark Humphrys Autumn: Mark Humphrys
Module Teachers	Mark Humphrys
NFQ level	8	Credit Rating	5
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

None
Array

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

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

*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

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 Assessment	25%	Examination Weight	75%

Type	Description	% of total	Assessment Date
Course Work Breakdown
Assignment	Internet based programming project to solve AI problem	25%	Once per semester

Reassessment Requirement Type
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
This module is category 1

Indicative Reading List

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

Other Resources

None

Programme or List of Programmes

CASE	BSc in Computer Applications (Sft.Eng.)
COMSCI	BSc in Computer Science
ECSA	Study Abroad (Engineering & Computing)
ECSAO	Study Abroad (Engineering & Computing)

Date of Last Revision

19-JAN-12

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