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Study-Unit Description
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TITLE Artificial Intelligence

LEVEL 05 - Postgraduate Modular Diploma or Degree Course


DEPARTMENT Cognitive Science

DESCRIPTION One way to understand a human cognitive system is to try to build an artificial model that duplicates some of its characteristics. Such a model can, under certain circumstances, have explanatory power. The field of Artificial Intelligence (AI) is targeted particularly at building computational models of those characteristics that constitute intelligence. Opinions differ on the nature of those characteristics and on the techniques used to model them.

This study-unit introduces fundamental concepts in AI from a theoretical and cognitive perspective. We will discuss questions concerning what "intelligence" is and whether machines can have intelligent properties.

Students will be introduced to a variety of typical AI problems in such areas as perception, reasoning, learning and language processing.

The study-unit will also relate AI to the field of cognitive modelling where the task is not necessarily to solve a real-world problem, but to create a computational simulation of a limited but nevertheless significant part of the problem.

The study-unit will be run as a three week intensive module:
Week 1: Reading
Week 2: Face-to-face lectures and practical workshops
Week 3: Solving an AI problem and writing a report

Study-unit Aims:

The aims of this study-unit are:

- To expose students to how AI relates to the broader scope of Cognitive Science by considering how AI can contribute to understanding natural intelligence;
- To provide concrete examples of AI problems and solution techniques.

Learning Outcomes:

1. Knowledge & Understanding:
By the end of the study-unit the student will be able to:

- explain essential characteristics of AI problems;
- explain the history of AI;
- compare different approaches to problem-solving in AI;
- relate AI principles to empiricist versus nativist positions in Cognitive Science;
- see whether a given AI approach is knowledge and data driven approaches.

2. Skills:
By the end of the study-unit the student will be able to:

- compare the appropriateness of a number of AI techniques to solve simple problems;
- see the relation between different AI techniques and different schools of thought in cognitive science (e.g., empiricist versus nativist positions) - Represent facts and knowledge using different knowledge representation schemes.

Main Text/s and any supplementary readings:

- Poole, D. and Mackworth, A. (2010). “Artificial Intelligence: Foundations of Computational Agents”. Cambridge University Press. Available on-line at
- Russel, S. and Norvig, P. (2009). “Artificial Intelligence: A Modern Approach”. Prentice Hall. 3rd Edition.

STUDY-UNIT TYPE Lecture, Independent Study & Tutorial

Assessment Component/s Resit Availability Weighting
Case Study (take home) Yes 100%

LECTURER/S Claudia Borg
Albert Gatt

The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints.
Units not attracting a sufficient number of registrations may be withdrawn without notice.
It should be noted that all the information in the study-unit description above applies to the academic year 2017/8, if study-unit is available during this academic year, and may be subject to change in subsequent years.

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