|Assessment Type||Assessment length / details||Proportion|
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.
This module is at CQFW Level 7