Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Multi-Robot Task Allocation in Uncertain Environments
Autonomous Robots
Analysis of Dynamic Task Allocation in Multi-Robot Systems
International Journal of Robotics Research
Coordinating hundreds of cooperative, autonomous vehicles in warehouses
IAAI'07 Proceedings of the 19th national conference on Innovative applications of artificial intelligence - Volume 2
Coalition formation for task allocation: theory and algorithms
Autonomous Agents and Multi-Agent Systems
Multi-robot coalition formation in real-time scenarios
Robotics and Autonomous Systems
Swarm-like Methodologies for Executing Tasks with Deadlines
Journal of Intelligent and Robotic Systems
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This paper presents a communication-less multi-agent task allocation procedure that allows agents to use past experience to make non-greedy decisions about task assignments. Experimental results are given for problems where agents have varying capabilities, tasks have varying difficulties, and agents are ignorant of what tasks they will see in the future. These types of problems are difficult because the choice an agent makes in the present will affect the decisions it can make in the future. Current task-allocation procedures, especially the market-based ones, tend to side-step the issue by ignoring the future and assigning tasks to agents in a greedy way so that short-term goals are met. It is shown here that these short-sighted allocation procedures work well in situations where the ratio of task length to team size is small, but their performance decreases as this ratio increases. The adaptive method presented here is shown to perform well in a wide range of task-allocation problems, and because it requires no explicit communication, its computational costs are independent of team size.