Ant algorithms for discrete optimization
Artificial Life
Stigmergy, self-organization, and sorting in collective robotics
Artificial Life
Collective Robotic Site Preparation
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Quorum sensing on mobile ad-hoc networks
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Learning from House-Hunting Ants: Collective Decision-Making in Organic Computing Systems
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Dependency by Concentration of Pheromone Trail for Multiple Robots
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Get in touch: cooperative decision making based on robot-to-robot collisions
Autonomous Agents and Multi-Agent Systems
Self-Organized Aggregation Triggers Collective Decision Making in a Group of Cockroach-Like Robots
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Re-embodiment of Honeybee Aggregation Behavior in an Artificial Micro-Robotic System
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Collective decision-making in decentralized multiple-robot systems: a biologically inspired approach to making up all of your minds
How two cooperating robot swarms are affected by two conflictive aggregation spots
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part II
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Intelligent entities must often make decisions by comparing several candidate alternatives and selecting the best one. This is just as true for autonomous swarms as it is for solitary robots, but to date there has been little work to propose efficient comparison behaviors for autonomous robotic swarms that are not tied to specific environments. In this work, we examine an elegant collective comparison strategy that is used by at least three different species of social insect and adapt it for artificial systems. The behavior is particularly attractive for robotic implementations because it relies only on short range explicit peer-to-peer communication, eliminating the need for chemical trails or other forms of stigmergy. The proposed comparison strategy is proven to converge, and a series of experiments using real robots with noisy sensors is presented that validates our theoretical analysis. Using the proposed behavior, a robotic swarm is able to compare alternatives collectively more accurately than its member robots would be able to individually.