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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.


Computational times for running each experiment in Fig. 5. CBBA assigns only single-agent tasks, the CBGA assigns multi-agent tasks, and mix requires both the CBGA and CBBA
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Fig6: Computational times for running each experiment in Fig. 5. CBBA assigns only single-agent tasks, the CBGA assigns multi-agent tasks, and mix requires both the CBGA and CBBA

Mentions: As expected, the computational times in Fig. 6 show the CBGA takes longer to come to a consensus, and this was probable due to the increased complexity of assignments. The CBBA solves single-agent assignments where each task will require 1 assignment. The CBGA solves multi-agent assignments where each task will require more than one assignment. The CBBA in Fig. 5 had to solve 20 assignments, 1 per task; alternatively, the CBGA had to solve 40 assignments because each task required 2 agents. Comparing computational time and number of communication steps, the CBGA takes a longer time to compute the consensus when receiving new assignments, but requires less overall communication between agents to achieve the final consensus.Fig. 6


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)

Computational times for running each experiment in Fig. 5. CBBA assigns only single-agent tasks, the CBGA assigns multi-agent tasks, and mix requires both the CBGA and CBBA
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig6: Computational times for running each experiment in Fig. 5. CBBA assigns only single-agent tasks, the CBGA assigns multi-agent tasks, and mix requires both the CBGA and CBBA
Mentions: As expected, the computational times in Fig. 6 show the CBGA takes longer to come to a consensus, and this was probable due to the increased complexity of assignments. The CBBA solves single-agent assignments where each task will require 1 assignment. The CBGA solves multi-agent assignments where each task will require more than one assignment. The CBBA in Fig. 5 had to solve 20 assignments, 1 per task; alternatively, the CBGA had to solve 40 assignments because each task required 2 agents. Comparing computational time and number of communication steps, the CBGA takes a longer time to compute the consensus when receiving new assignments, but requires less overall communication between agents to achieve the final consensus.Fig. 6

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.