Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
The impact of behavioural diversity in the evolution of multi-agent systems robust to dynamic environments
Behavioural diversity has been shown to be beneficial in biological social systems, such as insect colonies and human societies, as well as artificial systems such as large-scale swarm robotics applications. Evolutionary swarm robotics is a popular experimental platform for demonstrating the emergen...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | Eng |
| Published: |
Department of Computer Science
2024
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| Summary: | Behavioural diversity has been shown to be beneficial in biological social systems, such as insect colonies and human societies, as well as artificial systems such as large-scale swarm robotics applications. Evolutionary swarm robotics is a popular experimental platform for demonstrating the emergence of various social phenomena and collective behaviour, including behavioural diversity and specialisation. However, from an automated design perspective, the evolutionary conditions necessary to synthesise optimal collective behaviours that function across increasingly complex environments remains unclear. Thus, we introduce a comparative study of behavioural diversity maintenance methods (based on the MAP-Elites algorithm) versus those without behavioural diversity mechanisms (based on the steady-state genetic algorithm), as a means to evolve suitable degrees of behavioural diversity over increasingly difficult collective behaviour tasks. For this purpose, a collective sheep-dog herding task is simulated which requires the evolved robots (dogs) to capture a dispersed flock of agents (sheep) in a target zone. Different methods for evolving both homogeneous and heterogeneous swarms are investigated, including a novel approach for optimising swarm allocations of pre-evolved, behaviourally diverse controllers. In support of previous work, experiment results demonstrate that behavioural diversity can be generated without specific speciation mechanisms or geographical isolation in the task environment. Furthermore, we exhibit significantly improved task performance for heterogeneous swarms generated by our novel allocation evolution approach, when compared with separate homogeneous swarms using identical controllers. The introduction of this multi-step method for evolving swarm-controller allocations represents the major contribution of this work. |
|---|