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School of Engineering and Informatics (for staff and students)

Adaptive Systems (825G5)

Adaptive Systems

Module 825G5

Module details for 2022/23.

15 credits

FHEQ Level 7 (Masters)

Pre-Requisite

The module assumes an ability to write software in one appropriate programming language (e.g. Java, C, Python, Matlab). Basic knowledge of formal computational skills is also assumed.

Module Outline

This module, on the cross-disciplinary subject of the study of
adaptive systems, aims to provide students with an understanding of
various processes of adaptation which operate both in and upon natural
and artificial systems. To that end, in lectures and seminars we focus
on the introduction and discussion of how evolution, in biological and
artificial contexts, adapts systems to their environments or specified
tasks, and how self-adapting systems can adapt to cope with changing
environments or to acquire new skills. Lecture topics include: the
cybernetic origins of adaptive systems research, the central role of
feedback in intelligent and adaptive behaviour, ultrastable systems,
self-organised and emergent systems, autonomous and evolutionary
robotics, and the free energy principle and active inference.

Pre-Requisite

Mathematics & Computational Methods for Complex Systems (817G5) or
equivalent mathematical module / prior experience.

Library

Design for a brain: the origin of adaptive behaviour, by W. R. Ashby , 2 ed , Chapman, 1960.
Understanding intelligence, by Rolf Pfeifer and Christian Scheier, Cambridge, Mass.:MIT Press, 1999.
Principles of Self-Organization, by H. Von Foerster and G. W. Zopf, Pergamon Press, 1962.
The Mechanical Mind in History, by P. Husbands, O. Holland, and M. Wheeler, 2008.

NB: This list is by no means comprehensive. A full list is provided in the course site.

Module learning outcomes

Recognise, describe and model adaptive processes in natural and/or artificial systems.

Critically evaluate approaches to developing adaptive behaviour.

Demonstrate implementation-level familiarity with a variety of adaptive algorithms and techniques and apply them in problem solving biological modelling.

Deploy such techniques in a research project.

TypeTimingWeighting
Coursework100.00%
Coursework components. Weighted as shown below.
ReportA2 Week 1 80.00%
ReportT2 Week 8 20.00%
Timing

Submission deadlines may vary for different types of assignment/groups of students.

Weighting

Coursework components (if listed) total 100% of the overall coursework weighting value.

TermMethodDurationWeek pattern
Spring SemesterLaboratory2 hours00011110110
Spring SemesterLecture2 hours11111111111
Spring SemesterSeminar2 hours11100001001

How to read the week pattern

The numbers indicate the weeks of the term and how many events take place each week.

Dr Chris Johnson

Assess convenor
/profiles/246069

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The University reserves the right to make changes to the contents or methods of delivery of, or to discontinue, merge or combine modules, if such action is reasonably considered necessary by the University. If there are not sufficient student numbers to make a module viable, the University reserves the right to cancel such a module. If the University withdraws or discontinues a module, it will use its reasonable endeavours to provide a suitable alternative module.

School of Engineering and Informatics (for staff and students)

School Office:
School of Engineering and Informatics, ÄûÃÊÊÓƵ, Chichester 1 Room 002, Falmer, Brighton, BN1 9QJ
ei@sussex.ac.uk
T 01273 (67) 8195

School Office opening hours: School Office open Monday – Friday 09:00-15:00, phone lines open Monday-Friday 09:00-17:00
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