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A Consensus-Based Grouping Algorithm for Multi-agent Cooperative Task Allocation with Complex Requirements.

Hunt S, Meng Q, Hinde C, Huang T - Cognit Comput (2014)

Bottom Line: Inspiration is taken from the cognitive behaviours of eusocial animals for cooperation and improved assignments.Further extensions are provided to improve task complexity handling by the agents with added equipment requirements and task dependencies.Simulation results demonstrate reduced data usage and communication time to come to a consensus on multi-agent tasks.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Loughborough University, Loughborough, UK.

ABSTRACT
This paper looks at consensus algorithms for agent cooperation with unmanned aerial vehicles. The foundation is the consensus-based bundle algorithm, which is extended to allow multi-agent tasks requiring agents to cooperate in completing individual tasks. Inspiration is taken from the cognitive behaviours of eusocial animals for cooperation and improved assignments. Using the behaviours observed in bees and ants inspires decentralised algorithms for groups of agents to adapt to changing task demand. Further extensions are provided to improve task complexity handling by the agents with added equipment requirements and task dependencies. We address the problems of handling these challenges and improve the efficiency of the algorithm for these requirements, whilst decreasing the communication cost with a new data structure. The proposed algorithm converges to a conflict-free, feasible solution of which previous algorithms are unable to account for. Furthermore, the algorithm takes into account heterogeneous agents, deadlocking and a method to store assignments for a dynamical environment. Simulation results demonstrate reduced data usage and communication time to come to a consensus on multi-agent tasks.

No MeSH data available.


Average amount of data sent in progressive steps through a simulation. Simulated experiments contain 10 agents completing 20 tasks requiring L agents per task. Data are calculated as an individual piece of information sent from one agent. Markers (×) used to show the new data system
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Fig3: Average amount of data sent in progressive steps through a simulation. Simulated experiments contain 10 agents completing 20 tasks requiring L agents per task. Data are calculated as an individual piece of information sent from one agent. Markers (×) used to show the new data system

Mentions: With changes made to the data structures on each agent, comparisons can be made between the two different data storage methods. Adapting the original method to multi-agent tasks uses multiple vectors to store each bid. The new method uses a dynamic matrix for each agent. Assignments are sent individually in the form . Figure 3 shows that the new system reduces the amount of data sent for multi-agent tasks. Data sent with the new system gradually increases over the simulation. The old method involved sending the entire assignment data regardless of whether bids had been made. With the new system, redundant data are removed allowing agents to send only the required information.Fig. 3


A Consensus-Based Grouping Algorithm for Multi-agent Cooperative Task Allocation with Complex Requirements.

Hunt S, Meng Q, Hinde C, Huang T - Cognit Comput (2014)

Average amount of data sent in progressive steps through a simulation. Simulated experiments contain 10 agents completing 20 tasks requiring L agents per task. Data are calculated as an individual piece of information sent from one agent. Markers (×) used to show the new data system
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4150994&req=5

Fig3: Average amount of data sent in progressive steps through a simulation. Simulated experiments contain 10 agents completing 20 tasks requiring L agents per task. Data are calculated as an individual piece of information sent from one agent. Markers (×) used to show the new data system
Mentions: With changes made to the data structures on each agent, comparisons can be made between the two different data storage methods. Adapting the original method to multi-agent tasks uses multiple vectors to store each bid. The new method uses a dynamic matrix for each agent. Assignments are sent individually in the form . Figure 3 shows that the new system reduces the amount of data sent for multi-agent tasks. Data sent with the new system gradually increases over the simulation. The old method involved sending the entire assignment data regardless of whether bids had been made. With the new system, redundant data are removed allowing agents to send only the required information.Fig. 3

Bottom Line: Inspiration is taken from the cognitive behaviours of eusocial animals for cooperation and improved assignments.Further extensions are provided to improve task complexity handling by the agents with added equipment requirements and task dependencies.Simulation results demonstrate reduced data usage and communication time to come to a consensus on multi-agent tasks.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Loughborough University, Loughborough, UK.

ABSTRACT
This paper looks at consensus algorithms for agent cooperation with unmanned aerial vehicles. The foundation is the consensus-based bundle algorithm, which is extended to allow multi-agent tasks requiring agents to cooperate in completing individual tasks. Inspiration is taken from the cognitive behaviours of eusocial animals for cooperation and improved assignments. Using the behaviours observed in bees and ants inspires decentralised algorithms for groups of agents to adapt to changing task demand. Further extensions are provided to improve task complexity handling by the agents with added equipment requirements and task dependencies. We address the problems of handling these challenges and improve the efficiency of the algorithm for these requirements, whilst decreasing the communication cost with a new data structure. The proposed algorithm converges to a conflict-free, feasible solution of which previous algorithms are unable to account for. Furthermore, the algorithm takes into account heterogeneous agents, deadlocking and a method to store assignments for a dynamical environment. Simulation results demonstrate reduced data usage and communication time to come to a consensus on multi-agent tasks.

No MeSH data available.