Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
Adaptive Task Allocation Inspired by a Model of Division of Labor in Social Insects
Biocomputing and emergent computation: Proceedings of BCEC97
A Swarm Based Approach for Task Allocation in Dynamic Agents Organizations
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Multi-agent task allocation: learning when to say no
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Heterogeneous multirobot coordination with spatial and temporal constraints
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Consensus-based decentralized auctions for robusttask allocation
IEEE Transactions on Robotics
Robot Exploration Mission Planning Based on Heterogeneous Interactive Cultural Hybrid Algorithm
ICNC '09 Proceedings of the 2009 Fifth International Conference on Natural Computation - Volume 05
Strategies for energy optimisation in a swarm of foraging robots
SAB'06 Proceedings of the 2nd international conference on Swarm robotics
Coalition formation with spatial and temporal constraints
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 3 - Volume 3
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Very few studies have been carried out to test multi-robot task allocation swarm algorithms in real time systems, where each task must be executed before a deadline. This paper presents a comparative study of several swarm-like algorithms and auction based methods for this kind of scenarios. Moreover, a new paradigm called pseudo-probabilistic swarm-like, is proposed, which merges characteristics of deterministic and probabilistic classical swarm approaches. Despite that this new paradigm can not be classified as swarming, it is closely related with swarm methods. Pseudo-probabilistic swarm-like algorithms can reduce the interference between robots and are particularly suitable for real time environments. This work presents two pseudo-probabilistic swarm-like algorithms: distance pseudo-probabilistic and robot pseudo-probabilistic. The experimental results show that the pseudo-probabilistic swarm-like methods significantly improve the number of finished tasks before a deadline, compared to classical swarm algorithms. Furthermore, a very simple but effective learning algorithm has been implemented to fit the parameters of these new methods. To verify the results a foraging task has been used under different configurations.