Turtles, termites, and traffic jams: explorations in massively parallel microworlds
Turtles, termites, and traffic jams: explorations in massively parallel microworlds
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
Simultaneous Localization and Map-Building Using Active Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploring artificial intelligence in the new millennium
Ant Colony Optimization
International Journal of Robotics Research
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
Anytime optimal coalition structure generation
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Hierarchical SLAM: Real-Time Accurate Mapping of Large Environments
IEEE Transactions on Robotics
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Shout and Act (S&A) is an evolution of Bar Systems, a family of algorithms for different classes of complex optimization problems in static and dynamic environments by reactive multi agent systems. We adapt these systems to RoboRescue, where robots explore land looking for victims. When they find someone they “shout” so that robot mates can hear it. The louder the shout, the most important or urgent the finding. Louder shouts can also refer to closeness. Several experiments show that this system works very scalably, and how heterogeneous teams of robots outperform homogeneous ones over a range of task complexity. Finally, our results impact the design of RoboRescue teams: a properly designed combination of robots is cheaper and more scalable when confronted with uncertain maps of victims.