Self-Organization in Biological Systems
Self-Organization in Biological Systems
Noise-Induced Adaptive Decision-Making in Ant-Foraging
SAB '08 Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats
Get in touch: cooperative decision making based on robot-to-robot collisions
Autonomous Agents and Multi-Agent Systems
Re-embodiment of Honeybee Aggregation Behavior in an Artificial Micro-Robotic System
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Analysis of emergent symmetry breaking in collective decision making
Neural Computing and Applications - Special Issue on Theory and applications of swarm intelligence
Towards swarm calculus: universal properties of swarm performance and collective decisions
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
Collective robot navigation using diffusion limited aggregation
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
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Symmetry breaking is commonly found in self-organized collective decision making. It serves an important functional role, specifically in biological and bio-inspired systems. The analysis of symmetry breaking is thus an important key to understanding self-organized decision making. However, in many systems of practical importance available analytic methods cannot be applied due to the complexity of the scenario and consequentially the model. This applies specifically to self-organization in bio-inspired engineering. We propose a new modeling approach which allows us to formally analyze important properties of such processes. The core idea of our approach is to infer a compact model based on stochastic processes for a one-dimensional symmetry parameter. This enables us to analyze the fundamental properties of even complex collective decision making processes via Fokker-Planck theory. We are able to quantitatively address the effectiveness of symmetry breaking, the stability, the time taken to reach a consensus, and other parameters. This is demonstrated with two examples from swarm robotics.