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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.


Related in: MedlinePlus

Conflict resolution for the CBGA for multi-agent tasks. Consensus performed between two agents i and k updating agent bid list for agent i
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Fig2: Conflict resolution for the CBGA for multi-agent tasks. Consensus performed between two agents i and k updating agent bid list for agent i

Mentions: Phase 2 of the algorithm takes communications received from neighbours and analyses their knowledge of assignments to come to a consensus. Each agent communicates their winning bid matrix Xi and the time stamp si displaying the last information update from each of the other agents. As agents receive assignment data from neighbours, they will build up and store assignment matrixes for each neighbour where Xijm is defined as the bid agent i thinks agent m has made for task j. The consensus algorithm is split into two sections, the first section (line 4–6, Fig. 2) deals with tasks that require a single agent, using Lj to determine the number of agents required for task j. Tasks requiring a single agent will require the same consensus algorithm as found in the CBBA [11]. The consensus algorithm assumes only valid bids are made during the bundle construction algorithm, thus no changes are required for single-agent tasks. This paper focuses on tasks that require more than one agent and thus require a different consensus algorithm to converge on an answer for the multi-agent tasks.Fig. 2


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)

Conflict resolution for the CBGA for multi-agent tasks. Consensus performed between two agents i and k updating agent bid list for agent i
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig2: Conflict resolution for the CBGA for multi-agent tasks. Consensus performed between two agents i and k updating agent bid list for agent i
Mentions: Phase 2 of the algorithm takes communications received from neighbours and analyses their knowledge of assignments to come to a consensus. Each agent communicates their winning bid matrix Xi and the time stamp si displaying the last information update from each of the other agents. As agents receive assignment data from neighbours, they will build up and store assignment matrixes for each neighbour where Xijm is defined as the bid agent i thinks agent m has made for task j. The consensus algorithm is split into two sections, the first section (line 4–6, Fig. 2) deals with tasks that require a single agent, using Lj to determine the number of agents required for task j. Tasks requiring a single agent will require the same consensus algorithm as found in the CBBA [11]. The consensus algorithm assumes only valid bids are made during the bundle construction algorithm, thus no changes are required for single-agent tasks. This paper focuses on tasks that require more than one agent and thus require a different consensus algorithm to converge on an answer for the multi-agent tasks.Fig. 2

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.


Related in: MedlinePlus