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Learning and Measuring Specialization in Collaborative Swarm Systems
 

Summary: 199
Learning and Measuring Specialization in
Collaborative Swarm Systems
Ling Li
Learning Systems Group, California Institute of Technology
Alcherio Martinoli
Swarm-Intelligent Systems Research Group, EPFL
Yaser S. Abu-Mostafa
Learning Systems Group, California Institute of Technology
This paper addresses qualitative and quantitative diversity and specialization issues in the framework
of self-organizing, distributed, artificial systems. Both diversity and specialization are obtained via dis-
tributed learning from initially homogeneous swarms. While measuring diversity essentially quantifies
differences among the individuals, assessing the degree of specialization implies correlation between
the swarm's heterogeneity with its overall performance. Starting from the stick-pulling experiment in
collective robotics, a task that requires the collaboration of two robots, 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 types of reinforcement signals on performance,
diversity, and specialization in these collaborative experiments. Results show that, though diversity is
not explicitly rewarded in our learning algorithm, even in scenarios without explicit communication
among agents the swarm becomes specialized after learning. The degrees of both diversity and spe-

  

Source: Abu-Mostafa, Yaser S. - Department of Mechanical Engineering & Computer Science Department, California Institute of Technology

 

Collections: Computer Technologies and Information Sciences