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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.


Effects of adding task quitting and team rewards to the CBGA for multi-agent tasks. Experimental score used 10 agents completing 20 tasks where each task required 4 agents for successful completion
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Fig7: Effects of adding task quitting and team rewards to the CBGA for multi-agent tasks. Experimental score used 10 agents completing 20 tasks where each task required 4 agents for successful completion

Mentions: When addressing multi-agent tasks using an algorithm that focuses on individual improvement, additional agent incentive is required to increase the effectiveness of multi-agent assignments. Task quitting and team rewards were added, and their improvements can be seen in Fig. 7. Using either task quitting or team rewards produced more complete assignments which in turn provided a higher score. Implementing task quitting on its own provides an average increase of 206 with an average score of 5,962 ± 708. Another improvement of 144 can be achieved by assigning with respect to the team rewards over task quitting producing an average score of 6,106 ± 601 but this improvement is only significant to p < 0.15. Further improvements are gained from using both functions increasing the mean base CBGA score from 5,756 ± 722 to a mean score of 6,216 ± 634 with a statistical significance to p < 0.01.Fig. 7


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)

Effects of adding task quitting and team rewards to the CBGA for multi-agent tasks. Experimental score used 10 agents completing 20 tasks where each task required 4 agents for successful completion
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig7: Effects of adding task quitting and team rewards to the CBGA for multi-agent tasks. Experimental score used 10 agents completing 20 tasks where each task required 4 agents for successful completion
Mentions: When addressing multi-agent tasks using an algorithm that focuses on individual improvement, additional agent incentive is required to increase the effectiveness of multi-agent assignments. Task quitting and team rewards were added, and their improvements can be seen in Fig. 7. Using either task quitting or team rewards produced more complete assignments which in turn provided a higher score. Implementing task quitting on its own provides an average increase of 206 with an average score of 5,962 ± 708. Another improvement of 144 can be achieved by assigning with respect to the team rewards over task quitting producing an average score of 6,106 ± 601 but this improvement is only significant to p < 0.15. Further improvements are gained from using both functions increasing the mean base CBGA score from 5,756 ± 722 to a mean score of 6,216 ± 634 with a statistical significance to p < 0.01.Fig. 7

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.