Summary: 2nd International Workshop on the Mathematics and Algorithms of Social Insects
Diversity and Specialization in
Collaborative Swarm Systems
, Alcherio Martinoli2
, Yaser S. Abu-Mostafa1
1. California Institute of Technology, Pasadena, CA 91125, USA.
Corresponding author: firstname.lastname@example.org
2. Swiss Federal Institute of Technology, CH-1015 Lausanne, Switzerland.
This paper addresses qualitative and quantitative diversity and specialization issues in the frame-
work of self-organizing, distributed, artificial systems. Both diversity and specialization are ob-
tained via distributed learning from initially homogeneous swarms. While measuring diversity
essentially quantifies differences among the individuals, assessing the degree of specialization
implies to correlate the swarm's heterogeneity with its overall performance. Starting from a
stick-pulling experiment in collective robotics, a task that requires the collaboration of two ro-
bots, we abstract and generalize in simulation the task constraints to k robots collaborating
sequentially or in parallel. We investigate quantitatively the influence of task constraints and
type of reinforcement signals on diversity and specialization in these collaborative experiments.
Results show that, though diversity is not explicitly rewarded in our learning algorithm and there