Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Evolving mobile robots able to display collective behaviors
Artificial Life
Learning and Measuring Specialization in Collaborative Swarm Systems
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Efficient evaluation functions for evolving coordination
Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Open-ended evolution as a means to self-organize heterogeneous multi-robot systems in real time
Robotics and Autonomous Systems
Coevolution of Role-Based Cooperation in Multiagent Systems
IEEE Transactions on Autonomous Mental Development
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This paper deals with the problem of obtaining coordinated behavior in multirobot systems by evolution. More specifically, we are interested in using a method that allows the emergence of different species if they are required by the task, that is, if specialization provides an advantage in the completion of the task, without the designer having to predefine the best way to solve it. To this end, in this work we have applied a co-evolutionary algorithm called ASiCo (Asynchronous Situated Co-evolution) which is based on an open-ended evolution of the robots in their environment. In this environment the robots are born, mate and die throughout the generations as in an artificial life system. In order to show that ASiCo is capable of obtaining species automatically if they are advantageous, here we apply it to a collective gathering and construction task where homogeneous teams are suboptimal.