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.
| Type | Timing | Weighting |
|---|---|---|
| Coursework | 100.00% | |
| Coursework components. Weighted as shown below. | ||
| Report | A2 Week 1 | 80.00% |
| Report | T2 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.
| Term | Method | Duration | Week pattern |
|---|---|---|---|
| Spring Semester | Laboratory | 2 hours | 00011110110 |
| Spring Semester | Lecture | 2 hours | 11111111111 |
| Spring Semester | Seminar | 2 hours | 11100001001 |
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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