Evolving mobile robots able to display collective behaviors
Artificial Life
Efficient evaluation functions for evolving coordination
Evolutionary Computation
A Complex Systems Based Tool for Collective Robot Behavior Emergence and Analysis
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies
Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies
Coevolution of heterogeneous multi-robot teams
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Social learning for collaboration through ASiCo based neuroevolution
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Evolutionary neural networks for practical applications
Darwinian embodied evolution of the learning ability for survival
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
An algorithm for distributed on-line, on-board evolutionary robotics
Proceedings of the 13th annual conference on Genetic and evolutionary computation
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part II
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This paper deals with the problem of autonomously organizing the behavior of a multiagent system through a distributed approach based on open-ended natural evolution. We computationally simulate life-like dynamics and their evolution from the definition of local and low level interactions, as used in Artificial Life simulations, in a distributed evolutionary algorithm called ASiCo (Asynchronous Situated Coevolution). In this algorithm, the agents that make up the population are situated in the environment and interact in an open-ended fashion, leading to emergent states or solutions. The aim of this paper is to analyze the capabilities of ASiCo for obtaining specialization in the multiagent system if required by the task. Furthermore, we want to study such specialization under changing conditions to show the intrinsic self-organization of this type of algorithm. The particular task selected here is multi-robot collective gathering, due to the suitability of ASiCo for its application to real robotic systems.