Methods for task allocation via agent coalition formation
Artificial Intelligence
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Autonomous Agents and Multi-Agent Systems
Dynamic Pricing with Limited Competitor Information in a Multi-Agent Economy
CooplS '02 Proceedings of the 7th International Conference on Cooperative Information Systems
Dynamic Consumer Profiling and Tiered Pricing Using Software Agents
Electronic Commerce Research
On multi-robot task allocation
On multi-robot task allocation
Multi-Attribute Dynamic Pricing for Online Markets Using Intelligent Agents
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Performance of digital pheromones for swarming vehicle control
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Distributed task selection in multi-agent based swarms using heuristic strategies
SAB'06 Proceedings of the 2nd international conference on Swarm robotics
A multi-agent UAV swarm for automatic target recognition
DAMAS'05 Proceedings of the 2005 international conference on Defence Applications of Multi-Agent Systems
Auction-based multi-robot task allocation in COMSTAR
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Dynamic Pricing Algorithms for Task Allocation in Multi-agent Swarms
Massively Multi-Agent Technology
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We consider the problem of distributed task allocation across swarm units comprising a swarm-based system. We model task allocation as a dynamic pricing problem within a market setup. Experimental results within a simulated environment for a distributed automatic target recognition application show that the market-based task selection mechanism compares favorably with other heuristic-based task selection strategies.