Introduction.doc

Department of
Computer Science

Postgraduate Diploma/MSc
Autonomous Intelligent Systems
Session: 2009-2010

Contents
Introduction...................................................................................................................................................................1
This Booklet......................................................................................................................................................................1
Aims and Objectives.........................................................................................................................................................1
MSc Course Content and Structure..................................................................................................................................2
Assessment.......................................................................................................................................................................3
Resits.................................................................................................................................................................................3
Submission dates..............................................................................................................................................................4
Resubmissions..................................................................................................................................................................4
Part-time students.............................................................................................................................................................4

Modules................................................................................................................................................................................4

Part 1 of the Scheme:
CHM6120 Introduction to Intelligent Systems
CHM6220 Adaptive Behaviour from Natural Systems
CHM6320 Representation and Reasoning for Intelligent Systems
CHM6420 Machine Learning for Intelligent Autonomous Systems
CHM6520 Intelligent Autonomous Systems
PGM0120 Research Skills and Personal Development

Part 2 of the Scheme:
CHM6960 MSc Project (MSc Intelligent Autonomous Systems)

Introduction
This Booklet
This booklet is intended for students on the Postgraduate Diploma/MSc in Intelligent Autonomous Systems in the
Department of Computer Science, Aberystwyth University. It applies to students who start the course during the 2009-2010
academic session.
The first part describes the aims and objectives of the course, the structure, how it is assessed, and the relevant regulations.
The second part describes the individual modules in some detail.
This booklet should be read in conjunction with the department's Student Handbook for 2009-2010, with the relevant
regulations of the Aberystwyth University, and with the Aberystwyth University Enabling Regulations for Modular
Master’s Degrees. Those regulations specify minimal criteria that must be satisfied by any Modular MSc programme.
Individual institutions within the University normally adopt regulations that are stricter than the minimum in certain
respects but which are appropriate to the academic content of their courses and the context in which their students are
studying. These are the regulations that are described in this handbook.

Aims and Objectives - MSc in Intelligent Autonomous Systems
This MSc sets out to provide students with cutting-edge knowledge of one of the hottest, research and development topics in
the world at the moment – the issues surrounding the development of intelligent, autonomous systems. While it is natural to
think of such systems are being robotic machines, making their way through a real-world environment without external aid,
there are other applications too. In business, software bots are increasingly required to monitor and interact with other
systems over the internet. For example, a business supply chain has many elements (purchasing, transportation and
logistics, stock manufacturing and stock availability) and these all need to be interconnected in an intelligent, often
autonomous way in order to maximise efficiency.
This course studies the key technologies needed to build Intelligent Autonomous Systems. It takes a practical approach,
with each course being based on practical work, culminating in a summer project.
This scheme is designed for students who have received a grounding in computer programming in their first degree
(Min: 2:2). An applicant with no formal academic qualification will be accepted if the applicant is deemed by the
Department’s MSc Coordination Panel to have suitable professional experience. In addition, under some circumstances, it
may be possible for students to transfer into this degree by first taking appropriate undergraduate (contact the admissions
tutor). Specifically, students who have successfully completed the course will:

• Have a deep understanding of the issues surrounding the building of intelligent and autonomous systems.
• Have an understanding of the current state-of-the-art in AI research and autonomous systems.
• Demonstrate the ability to build intelligent autonomous systems.
• Understand how autonomy and artificial intelligence can be applied to the fields of robotics and bioinformatics.
• Be able to carry out independent research, and understand the process of academic research.

The course demands a high level of commitment. Many students will find that they need to spend some 40 to 60 hours per
week. Each 20-credit module requires 200 hours of study, and each module lasts for just five weeks. Modules are based
around a seminar style of learning, combining aspects of lectures, workshops, practical sessions and tutorials in a each
seminar session. Self study is required, and students should be able to motivate themselves and organise their time
effectively, leading to timely completion of coursework, revision and examinations. Those who, for whatever reason, cannot
give this level of commitment should not embark on the course. Note that this course begins on 28rd September, 2009.

1

Course Content and Structure
In order to qualify for the award of an MSc in Intelligent Autonomous Systems, a student must obtain 180 credits. These
credits will normally be obtained by studying the modules prescribed by the department, i.e. those described in the second
part of this booklet. In exceptional circumstances, and with the agreement of the Head of Department, equivalent credits
from other modules may be substituted.
On successful completion (see later for details) of 120 credits students may elect to receive a diploma in Intelligent
Autonomous Systems, rather than continuing to pursue the dissertation with the view to being awarded the MSc.
For any applicant it is important that, from prior experience, he/she can already design and implement and can employ
suitable data structures and algorithms when solving a computing problem. If the applicant is missing one or more of these
abilities he/she, prior to joining the scheme, will be required to attend one or more undergraduate modules, or gain the
experience from similar modules offered elsewhere.

The course has the following structure:
1. Introduction to Intelligent Systems (CSM6120) :: Sept’09 – mid-Oct’09
An introductory module, taken at a faster pace than one would do it for undergraduate, that ensures each student is
aware of, and able in, the key themes and tools required for the rest of the course.
2. Adaptive Behaviour (CSM6220) :: mid-Oct’09 – beginning Dec’09
Material on artificial life, how adaptation works in nature, how they adapt to changes in their surroundings, and
how the underlying principles can be applied to artificial systems, and systems that can optimise their performance
3. Representation and Reasoning for Intelligent Systems (CSM6320) :: beginning Dec’09 – beginning Feb’10
Material on how to constrain systems to help them reason more effectively about the environment in which they
live; how systems can cope with uncertainty and how systems can reason in qualitative, modal-based and case-
based ways.
4. Machine Learning for Intelligent Autonomous Systems (CSM6420) :: beginning Feb’10 – mid-March’10
Material on how systems can learn from experience, and improve performance, including probabilistic methods,
parametric methods, non-parametric methods and approaches ensemble learning and meta-learning.
5. Intelligent Autonomous Systems (CSM6520) :: mid-March’10 – end Apr’10
Investigates how systems can function effectively while requiring minimal interaction with a human agent. Prac-
tical work in each of the courses research areas, designed to lead the student into their dissertation.
6. Research Training and Methods (PGM0120) :: Sept’09 – Apr’10 (across both semesters)
Ensures the students have research and study skills required to produce their research-based dissertation. The
module will be practical, so students can learn effective research skills.
7. Project Module: (CSM6960) :: May – end August
Develops and proves research skills.

Modules 1 – 6 are called “Part 1”; module 7 is called “Part 2”.
It is possible to follow the course on a part-time basis; this is described later in this document.

2

Assessment
1. To qualify for progression to Part Two (the dissertation/project phase) a candidate must obtain:
i. an average of at least 50 overall;
ii. marks of 50 or above in at least 80 credits of the modules taken in Part One, including any of the scheme's core
modules which have been specified by the Department as having to be passed with a minimum of 50.

2. To achieve Distinction level in Part One (the taught part of the course) a candidate must obtain:

i. an average of at least 70 overall;
ii. marks of 50 or above in at least 80 credits of the modules taken in Part One, including any core requirements as
specified by Departments.

3. In order to gain a Master’s degree a candidate must pass Part One and Part Two.

4. In order to gain a Master's Degree with Distinction, a candidate shall achieve an overall mark of not less than 70%,
having achieved not less than 65% in Part One and not less than 70% in Part Two. [In calculating the overall mark,
Part One and Part Two are equally weighted].

5. Candidates who have failed Part One or Part Two at the first attempt shall not be eligible for the award of Distinction.
6. Merit shall be awarded to master's candidates achieving an average of 60% or above over Parts One and Two of the
degree, and passing both parts at the first attempt, but who did not meet the requirements for distinction.
7. To qualify for the award of a Postgraduate Diploma a candidate must obtain:

(i) an average of at least 50 overall over 120 taught credits;
(ii) marks of 50 or above in at least 80 credits’ worth of modules in Part One including any modules which have been
specified as core for the Postgraduate Diploma.

A candidate who has attained an overall mark of 70% or above shall be eligible for the mark of Distinction. A
candidate who has qualified to progress to Part Two may, if they wish, elect to take a Diploma. A Diploma may also be
awarded to a candidate who fails to submit a dissertation within the approved time limit; or submits a dissertation that
is judged not to be of sufficient quality to merit the award of the MSc and fails to submit a revised dissertation of
suitable standard within the approved time limit.
8. To qualify for the award of a Postgraduate Certificate a candidate must obtain:

(i) an average of at least 50 overall over 60 taught credits;
(ii) marks of 50 or above in at least 40 credits’ worth of modules in the 60 taught credits assessed for the Certificate,
including any modules which have been specified as core for the Certificate.
9. Candidates re-sitting failed modules shall be eligible for a maximum of 50% in each.
The way in which individual modules are assessed is described under the detailed description of each module, in the second
part of this booklet.

Re-sits
At the discretion of the Examining Board, candidates who have failed to achieve the marks necessary for the award of an
MSc, or of a Postgraduate Diploma, at the end of Part One may be allowed to re-sit all or part of the assessment of these
modules, once only, during the Supplementary Examination period, in order to reach the standard required either for the
award of the diploma or to be allowed to proceed to the dissertation phase of the MSc. The maximum mark that may be
obtained when re-sitting failed modules is 50%, and these candidates are no longer able to gain a distinction.

3

Submission dates
Full-time students who begin the MSc in Internet and Distributed Systems in September 2009 must submit their
dissertations by 31st August 2010. If the dissertation is not submitted by this date, the dissertation will be treated as having
failed by non-submission.

Resubmissions
If a dissertation is submitted on time but fails on first submission, the candidate may re-present once only, not more than
twelve months from the date of the official communication to the candidate of the result by the University Registry.
If a dissertation is deemed to have failed as a result of non-submission by the due date, it may be resubmitted on one
occasion only, no more than twelve months after the date by which the first submission was formally due, for a mark of at
most 50%
Note that a distinction cannot be awarded if the dissertation has been resubmitted.

Part-time students
It is, in principle, possible to take the courses in part-time mode but this is only likely to be practicable for students whose
timetable is flexible and who can easily get to the campus during the day. The structure of the courses in part-time modes is
as follows:
Introduction to Intelligent Systems (CSM6120) :: Sept’09 – mid-Oct’09
Representation and Reasoning for Intelligent Systems (CSM6320) :: beginning Dec’09 – beginning Feb’10
Machine Learning for Intelligent Autonomous Systems (CSM6420) beginning Feb’10 – mid-March’10
Research Training and Methods (PGM0120) Sept’09 – Apr’10 (across both semesters of year 1)
Adaptive Behaviour (CSM6220) :: mid-Oct’10 – beginning Dec’10
Intelligent Autonomous Systems (CSM6520) mid-March’11 – end Apr’11
Project Module: (CSM6960) :: May’11 – end August’11

Please Note: part-time students will effectively need to be full-time for the duration of each module.
Students must submit their dissertations by 31st August 2011; failure to do so will automatically fail the dissertation part of
the MSc due to non-submission.
If a dissertation is submitted but fails on first submission, the candidate may re-present once only, not more than twelve
months from the date of the official communication to the candidate of the result by the University Registry.
If a dissertation is deemed to have failed as a result of non-submission by the due date, it may be resubmitted on one
occasion only, no more than twelve months after the date by which the first submission was formally due, for a mark of at
most 50%.
Note that a distinction cannot be awarded if the dissertation has been resubmitted.

Modules
The rest of this booklet reproduces module descriptions (as found on the web) for each module offered by the department
as part of this scheme. These descriptions are provided to help you understand what each module will entail. The numbers
of lectures given against each item in the syllabus give an idea of the relative weight of the topic in the module as a whole.
Lecturers will sometimes vary the number of lectures to respond to the needs of the class or to accommodate an alternative
presentation of the topic.

4

Module Identifier CHM6120
Module Title Introduction to Intelligent Systems
Academic Year 2009-2010
Co-ordinator Dr Simon Garrett
Semester Semester 1
Pre-Requisite Available only to students taking the MSc in Intelligent Autonomous Systems scheme.
Course delivery Seminar: 30hrs Practicals: 10hrs
Assessment Assessment Type Assessment Length/Details Proportion
Presentation and discussion of analytic report on scientific
Semester Presentation 20%
paper(s)
Semester Assessment Programming-oriented Intelligent Systems assignment 80%
Supplementary Exam Will take the same form as the original assessment 100%

Learning outcomes
On successful completion of this module students should:
1. Describe and use the basic principles of Artificial Intelligence and Machine Learning,
2. Be able to reflect on project needs,
3. Practically apply AI and ML principles to meet those needs.
4. Present the material they have learned in an informed, clear manner,
5. Demonstrate understanding and insight into the material that they are presenting.
Brief description
This module introduces the key ideas in Artificial Intelligence and ensures all students are at roughly the same level before
moving on to the specialist modules.
Content
1. Introduction (3 hrs) General introduction to Artificial Intelligence (AI), including discussion of what AI is, its
history, definitions, and philosophical debates on the issue (the Turing test and the Chinese room). Ethical
issues.
2. Search (6 hrs) Why search is important in AI and how to go about it. This includes both informed and
uninformed strategies. Evolutionary search.
3. Knowledge Representation (4 hrs) Ways of representing knowledge in a computer-understandable way.
Semantic networks, rules. Examples of the importance of KR.
4. Neural networks and subsymbolic learning (5hrs) We can find solutions using search, but how can we
remember solutions, learn from them and adapt them to new situations? This will cover perceptrons, single-layer
and multi-layer networks.
5. Propositional and First-Order Logic (4 hrs)
a. The backbone of knowledge representation.
6. Programming for Intelligent Systems (3 hrs)
a. Practical introduction to programming for Intelligent Systems, used to illustrate search, KR and first-
order logic.
7. Rule-based systems (3 hrs)
a. How can human expertise be automated? How to build an expert system - system concepts and
architectures. Rule-based systems: design, operation, reasoning, backward and forward chaining.
8. Knowledge Acquisition and its importance in KR and RBS. (2 hrs)

Reading List
Russell, S. and Norvig, P. (1995) Artificial Intelligence: A Modern Approach Prentice Hall..
Various research papers

5

Module Title Adaptive Behaviour from Natural Systems
Semester Semester 1
paper(s)
Semester Assessment Essay – topic in Intelligent Systems 20%

Learning outcomes
1. Apply simulation as a tool for inspiration and analysis in approaching complex phenomena.
2. Overcome linear thinking paradigm through examples from biology, social behaviour, economics etc.
3. Understand adaptive behaviour as a process (interaction between an entity and its environment) rather than an algorithm.
4. Understand the basics of dynamical systems theory.

Brief description
This module contains a description of adaptive behaviour in terms of (i) systems that changes over time
(behaviour), and (ii) change of a system’s behaviour with respect to results of the interaction between
environment and system (adaptation). It introduces the processes of adaptation, both on individual/population
level, different time scales, and indirectly via changing the environment. It examines adaptive behaviour in
biological systems (incl. ecosystems), individual development, agents and interactions, groups, societies,
economies, etc.

The module explores mechanisms of adaptive behaviour, including: centralised vs. decentralised organisation
principles, emergent phenomena, self-organization as mechanisms of adaptation and behaviour.

Finally, the module uses robot examples as tool to outline adaptive behaviour as a multi-objective adaptation
process. It analyses systems in which non-linear interaction, positive feedback, noise are acting as constructive
elements.

Content
1. Introduction – 3hrs
Key concepts, Aims and objectives; Introduction of the context used in this module (the problem of optimization);
Dynamical systems theory, basics.
2. Artificial life – 3hrs
Cellular automata, concepts of autonomy (autopoiesis) and embodied cognition exemplified in Game of Life;
complex systems; self-reproducing machines, with (video) examples from recent conferences.
3. Bio-Inspired Adaptive Systems (1) – 5 hrs
Structure and Process metaphors. Ideas drawn from animal anatomy and processes, Computational modelling of
Brain and neural systems, Artificial Immune systems and Endocrine Systems. The brain is a dynamical system.
Evolutionary metaphors, Basic ideas, hill-climbing and simulated annealing, search improvement, GA for bit
string representations, ES for real number representation and self-optimisation, GP, designing algorithms for real
world problems including multi-objective functions and dynamic functions, case studies: evolutionary robotics and
financial market analysis.
Developmental metaphors Development as evolution of the individual, staged growth, constraint functions,
algorithmic approach, examples from Epigenetic-robotics.
6. Adaptation from swarms and colonies – 6 hrs
Swarms – concepts, flocking behaviour, communication and control, simulations; stigmergy, synchronisation
(fireflies); Ant colonies / ACO (ant colony optimization)– motivation, implementation and applications for NP-
hard problems; concepts, search algorithms; Swarm-robotics.

Reading List
Russell, S. and Norvig, P. (1995) Artificial Intelligence: A Modern Approach Prentice Hall..

6

Module Title Representation and Reasoning for Intelligent Systems
Semester Semester 1
Analytic report on scientific paper(s) – 3000words
Semester Assessment 40%
including presentation and discussion
Semester Assessment Essay – 3000 words 60%

Learning outcomes
1. A good understanding of how intelligent systems can represent, reason and react effectively about the
environment in which they exist.
2. Appreciation of the diversity of the existing techniques for knowledge representation and inference as well
as their respective strengths and limitations.
3. Awareness of the current state-of-the-art in both symbolic and semi-symbolic approaches for reasoning and
revision with formally represented domain knowledge.
4. The ability and interest in applying advanced representation and reasoning techniques in solving real-world
problems.
5. The ability to search for, and critically evaluate literature relevant to their assignment topic, as demonstrated in
their assignment report.

Brief description
The module will present a variety of advanced topics relevant to the building of practical intelligent systems.
In addition to standard knowledge representation and reasoning techniques, it will cover most recent
developments as well. The module will introduce the keep concepts taken by individual approaches and
discuss the pros and cons of them. Students will be required to carry out independent review of carefully
selected research papers and to present their findings in class seminars.

Content
1. Constraint based techniques: (
(4 hrs)Ideas of constraint
satisfaction problems and typical algorithms for constraint satisfaction, constraint propagation and other
solution techniques.
2. Uncertainty handling techniques: (
(6 hrs)Example
theories and their utility of representing and reasoning with uncertain knowledge, including Bayesian nets,
Dempster-Shafer theory, fuzzy logic and rough set theory.
3. Symbolic belief revision techniques: (
(4 hrs)Techniques for an
intelligent system to make hypotheses and explore their consequences, covering reason maintenance and
assumption-based truth maintenance.
4. Qualitative reasoning: (
(4 hrs)Basic approaches for
qualitatively representing and reasoning about the structure and behaviour of domain systems, focussing on
the constraint-centred modelling paradigm.
5. Model based reasoning: (
(4 hrs)Methods and tools for
developing systems that utilise explicit models of domain problems, analysing the general diagnostic engine
and its extensions, and systems for failure mode and effects analysis.
6. Case based reasoning: (
(4 hrs)Principles and basic
techniques for exploiting knowledge of experienced cases, discussing important issues of case indexing, case
retrieval and case adaptation in such systems.
7. Example applications (4 hrs) of the different techniques/approaches

Reading List
- Q. Shen. Practical Reasoning Methodologies. (an unpublished book covering most of the above topics, expanded and updated
use as the main reference for students.)
- C. Price. Computer-Based Diagnostic Systems. Springer-Verlag, 1999.
- R. Jensen and Q. Shen. Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches. IEEE and Wiley &
2008.
- + State-of-the-art research papers.

7

Module Title Machine Learning for Intelligent Autonomous Systems
Semester Semester 2
Written assessment of scientific paper(s) -3000 words,
Semester Assessment 20%
followed by oral presentation and discussion of the same.
Written assessment, contrasting the use of two machine
Semester Assessment learning methods discussed in the course, applied to data 20%
provided by the lecturer.

Learning outcomes
1. Demonstrate competence with the implementation methods and tools used in the development of the types of autonomou
tem considered in this scheme.
2. Show proficiency in building autonomous systems using the appropriate tools.
3. Demonstrate skills in designing, running and documenting experiments using autonomous systems.
4. Demonstrate capability to write a detailed project proposal.

Brief description
This module will equip students with the main concepts in Machine Learning by engaging them in seminar-based
discussions on scientific papers. It will then help the students build towards a term paper, which will describe
their practical investigation of the issues involved in applying two machine learning methods to an appropriate
data set that they will have found.
Content
The content will closely follow Alpaydin’s book, with additional use of Mitchell’s book. The lectures will
introduce the ideas, and the students will be expected to read further from the book. This will be tested by
getting them to do presentations on sections of the book not covered in class.

1. What is Machine Learning? (2 hrs)
Foundations and assumptions of ML.
2. Supervised Learning. (
(2 hrs)
Learning from labelled examples.
3. Bayesian Decision Theory. (
(3 hrs)
Probability and optimality in learning.
4. Parametric Methods. (2 hrs)
5. Dimensionality Reduction. (
(2 hrs)
Detecting unnecessary attributes and removing them to improve accuracy.
6. Clustering. (
(3 hrs)
K-means, hierarchical, consensus clustering techniques.
7. Nonparametric Methods. (
(3 hrs)
Learning without constructing a model (esp. kNN); transductive learning.
8. Hidden Markov models. (
(3 hrs)
Probabilistic, structural models from data.
9. Assessing and Comparing Classification Algorithms. (3 hrs)
10. Combining Multiple Learners. (
(3 hrs)
Obtained improved results by combining the predictions of multiple classifiers.
11. Reinforcement Learning. (
(2 hrs)
Learning sequences of actions with reward.
12. Additional material as requested by students via questionnaire. (2 hrs)

Reading List
Alpaydin, E. (2004) Introduction to Machine Learning MIT Press.

8

Module Title Intelligent Autonomous Systems
Semester Semester 2
Assignments on laboratory work (three or four, weighting
split equally) all based on practical/seminar work and
Semester Lab Work 100%
designed to show the understanding of the student and
their growing ability to do research

Learning outcomes
1. Demonstrate competence with the implementation methods and tools used in the development of the types of autonomous sys
considered in this scheme.
2. Show proficiency in building autonomous systems using the appropriate tools.
3. Demonstrate skills in designing, running and documenting experiments using autonomous systems.
4. Demonstrate capability to write a detailed project proposal.

Brief description
Students will participate in lab work designed to given them practical experience of the various types of intelligent
autonomous system that are considered in this course. They will work in pair or threes on 4 short projects that will
introduce them to the tools and techniques that are used in the development of autonomous systems. Both robotic
and software systems will be included and students will be encouraged to think beyond the immediate problems in
order to develop an in-depth understanding of the topic areas such that they can then make well-informed
decisions about their dissertation topic. Students will then write a proposal for their dissertation work, based on
their lab work in this module.

Content
1. Properties of environments (
(2 hrs)
Diversity, change, predictability. Examples of different types of environment and how they affect autonomous
systems within them.
2. Strategies for dealing with diversity, change and predictability (2 hrs)
Reflect back on building world models, reactive systems and how the example environments impact on their
usefulness.
3. Tools for building systems: (5 hrs)
a. Robot programming APIs, testing, deployment and lab technique
b. Software techniques for autonomous systems
4. Introduction to laboratory projects. (1 hrs)
5. Short laboratory projects
Developing robotic and software autonomous systems to fulfil particular roles. (40 hrs in lab)
6. Prepare dissertation proposal (3 x 1hr tutorials)

Reading List
Various research papers (conference papers, journal papers, etc) specific to each project.

9

Module Identifier PGM0120
Module Title Research Skills and Personal Development
Co-ordinator Dr. Reyer Zwiggelaar
Semester Semester 1 and 2
Pre-Requisite
Course delivery Lectures: 18hrs
paper(s)
Semester Assessment Essay – topic in Intelligent Systems 20%

Learning outcomes
1. Assignments on laboratory work (three or four, weighting split equally) all based on practical/seminar work and
designed to show the understanding of the student and their growing ability to do research
2. Demonstrate a range of bibliographic and computing skills
3. Critically assess the ethical and legal issues involved in research practice, including issues of intellectual property
rights
4. Demonstrate skills in a range of dissemination strategies, including writing, oral presentation, internet usage,
media usage
5. Demonstrate an awareness of the key skills involved in teaching in a higher education context
6. Work as part of a team
7. Show an appreciation of the issues involved in managing a long-term research project

Brief description
This module aims to give research students a broad knowledge of a range of transferable skills that they can apply
in a variety of research interests. In particular, it will develop the ability of students to undertake independent
research projects. To this end, students will be required to submit independent work, which is linked to their own
particular research topic, and their ability to formulate and manage independent research projects will be assessed.
The module will also cover personal development. Students will be given skills in negotiating and networking,
teaching skills, presentation skills. The module also contains IT skills, both for general career development and in
an applied research context.

Content / Skills Developed
1. Awareness of the range of sources appropriate to specific research areas;
2. Knowledge of processes of academic communication;
3. Awareness of value of information management skills for effective research;
4. Demonstrated competence in the use of word-processing, e-mail and electronic databases.
5. Awareness of the elements required to design and manage research for a thesis;
6. Ability to manage study time and exploit the supervisory relationship effectively;
7. Understanding of the various approaches to writing;
8. Awareness of oral presentation strategies.
9. Identification the relevant legal and ethical issues which may arise within individual fields of research activity;
10. Understanding of the legal and ethical debates relating to research activity;
11. Knowledge of how to gain access to relevant legal materials relating to issues such as confidentiality, data
protection and the rules relating to the exploitation of intellectual property.

Reading List
Module Details and reading list can be found at: http://www.aber.ac.uk/postgrads/en/mod1.pdf

10

Module Title MSc Project (Intelligent Autonomous Systems)
Semester Semester 3 (Summer)
Course delivery Meetings with supervisor: 15hrs Private study: 600hrs
Dissertation and viva presentation, with submission
Semester Assessment compulsory by the end of September. Word limit of 100%
20,000 words (min 10,000)
Supplementary Will take the same form as the original assessment 100%

Learning outcomes
1. Identify and document requirements for a particular type of Intelligent Autonomous System, in a research project
context;
2. Use the professional and academic literature to survey existing approaches to the construction of such a system,
and define a novel approach that builds on existing approaches;
3. Develop a substantial piece of software to meet the identified requirements;
4. Design and carry out a set of validation, verification and testing activities to demonstrate that the software and/or
hardware produced does indeed meet the identified requirements;
5. Critically reflect on the choice of techniques and the manner of their use, in the light of the experience gained from
developing the software and/or hardware;
6. Identify weaknesses and lacunae in the available techniques;
7. Document all of the above to a professional, publishable standard;
8. Communicate understanding of each of points 1-7.

Brief description
This module forms a core part of the new Intelligent Autonomous Systems MSc. Within this module students
complete their MSc project and associated dissertation. The project must be on a topic related to the content and
learning outcomes of the MSc. Projects will be vetted for suitability by a member of staff. There will be a strict
hand-in cut-off point of the end of September following the October of entry (for full-time) and the second
September following the October of entry (for part-time).

Content
1.The MSc project will give the students the opportunity to pull together all they have learned in the course, and then
to apply it to a real-world, novel problem. There need not be the same degree of novelty as in a higher degree such as a
PhD, but we will require some novelty.
2.The project will also allow students to show that they can use professional and academic literature to extend their
knowledge to meet the challenges of the project.
3.Furthermore, in their final report, they will successfully critically evaluate other people's work, and use reflections
upon this to inform their own work.
4.As a result, the project sets out to help the students grow into a role as a researcher, and will stretch them in the skills
required to fill this role.

11

Introduction.doc

Recommended

Recommended

More Related Content

Similar to Introduction.doc

Similar to Introduction.doc (20)

More from butest

More from butest (20)

Introduction.doc