Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Anthill: A Framework for the Development of Agent-Based Peer-to-Peer Systems
ICDCS '02 Proceedings of the 22 nd International Conference on Distributed Computing Systems (ICDCS'02)
On multi-robot task allocation
On multi-robot task allocation
Learning and Measuring Specialization in Collaborative Swarm Systems
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Performance of digital pheromones for swarming vehicle control
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Swarm Intelligence in Data Mining (Studies in Computational Intelligence)
Swarm Intelligence in Data Mining (Studies in Computational Intelligence)
Building patterned structures with robot swarms
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Methods for task allocation via agent coalition formation
Artificial Intelligence
A multi-agent UAV swarm for automatic target recognition
DAMAS'05 Proceedings of the 2005 international conference on Defence Applications of Multi-Agent Systems
Market-Based Distributed Task Selection in Multi-agent Swarms
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
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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Swarm-based systems have emerged as an attractive paradigm for implementing distributed autonomous systems for various applications in commercial, military and business domains. One of the major operations in a swarm-based system is to ensure that the individual swarm units process the tasks in the environment in an efficient manner. This can be achieved using a suitable task selection mechanism that allocates the desired number of swarm units to each task while reducing inter-task latencies and communication overhead, and, ensuring adequate commitment of resources to tasks. In this paper, we describe a multi-agent based distributed task selection mechanism for swarm-based systems. We show that the distributed task selection problem is NP-complete and propose polynomial-time heuristic-based algorithms. Our simulation results show that heuristics in which each swarm unit considers both the effects of other swarm units on tasks and its own relative position to other swarm units achieve better task processing efficiency and improved distribution of swarm units over tasks.