|Delivery Type||Delivery length / details|
|Assessment Type||Assessment length / details||Proportion|
|Semester Assessment||Presentation and discussion of analytic report on scientific paper(s)||40%|
|Semester Assessment||Essay topic on Adaptive Behaviour (3000 words)||60%|
|Supplementary Assessment||Will take a form as agreed by the Department||100%|
On successful completion of this module students should be able to:
Apply simulation as a tool for inspiration and analysis in approaching complex phenomena.
Overcome linear thinking paradigm through examples from biology, social behaviour, economic
Understand adaptive behaviour as a process (interaction between an entity and its environment) rather than an algorithm.
Understand the basics of dynamical systems theory.
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.
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
2. 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 as a dynamical system.
3. Bio-Inspired Adaptive Systems (2) [10 hrs]
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.
5. Bio-Inspired Adaptive Systems (3) [3 hrs]
Development as evolution of the individual, staged growth,
constraint functions, algorithmic approach, examples from
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.
|Skills Type||Skills details|
|Application of Number||Inherent to subject|
|Improving own Learning and Performance||Inherent to subject|
|Information Technology||Inherent to subject|
|Personal Development and Career planning||Encourages students to see roles in subject for career and personal deveopment|
|Problem solving||Inherent to subject|
|Subject Specific Skills||Advanced Artificial Intelligence Skills|
Reading ListGeneral Text
Bonabeau, Eric. (1999.) Swarm intelligence :from natural to artificial systems /Eric Bonabeau, Marco Dorigo, Guy Theraulaz. Oxford University Press Primo search Bonabeau, Eric. (1999.) Swarm intelligence :from natural to artificial systems /Eric Bonabeau, Marco Dorigo, Guy Theraulaz. Oxford University Press Primo search Coley, David A. (c1999.) An introduction to genetic algorithms for scientists and engineers /David A. Coley. World Scientific Primo search De Jong, Kenneth A. (2006.) Evolutionary computation :a unified approach /Kenneth A. De Jong. MIT Press Primo search De Jong, Kenneth A. (c2006.) Evolutionary computation :a unified approach /Kenneth A. De Jong. MIT Press Primo search Eberhart, Russell C. (2001.) Swarm intelligence /Russell C. Eberhart, James Kennedy with Yuhui Shi. Morgan Kaufmann ; Primo search Eiben, Agoston E. (2003.) Introduction to evolutionary computing /A.E. Eiben, J.E. Smith. Springer Primo search Fogel, David B. (1995.) Evolutionary computation :toward a new philosophy of machine intelligence /David B. Fogel. IEEE Press Primo search Mitchell, Melanie. (1998 (2002 prin) An introduction to genetic algorithms /Melanie Mitchell. 1st MIT Press paperback ed. MIT Press Primo search Should Be Purchased
Luke, Sean (2011) Essentials of Metaheuristics http://cs.gmu.edu/~sean/book/metaheuristics/ textbook
This module is at CQFW Level 7