Multiagent Task Allocation in Complementary Teams: A Hunter-and-Gatherer Approach

Consider a dynamic task allocation problem, where tasks are unknowingly distributed over an environment. This paper considers each task comprising two sequential subtasks: detection and completion, where each subtask can only be carried out by a certain type of agent. We address this problem using a...

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Bibliographic Details
Published in:Complexity (New York, N.Y.) N.Y.), 2020, Vol.2020 (2020), p.1-15
Main Authors: Zargarzadeh, Hassan, Myler, Harley, Moazami, Saeed, Dadvar, Mehdi
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Communication
Computer simulation
Cooperation
Coupling
Decision making
Equilibrium analysis
Game theory
Investigations
Methods
Multiagent systems
Negotiations
Robots
Statistical analysis
Teams
Unmanned aerial vehicles
Workload
Workloads
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Summary:Consider a dynamic task allocation problem, where tasks are unknowingly distributed over an environment. This paper considers each task comprising two sequential subtasks: detection and completion, where each subtask can only be carried out by a certain type of agent. We address this problem using a novel nature-inspired approach called “hunter and gatherer.” The proposed method employs two complementary teams of agents: one agile in detecting (hunters) and another skillful in completing (gatherers) the tasks. To minimize the collective cost of task accomplishments in a distributed manner, a game-theoretic solution is introduced to couple agents from complementary teams. We utilize market-based negotiation models to develop incentive-based decision-making algorithms relying on innovative notions of “certainty and uncertainty profit margins.” The simulation results demonstrate that employing two complementary teams of hunters and gatherers can effectually improve the number of tasks completed by agents compared to conventional methods, while the collective cost of accomplishments is minimized. In addition, the stability and efficacy of the proposed solutions are studied using Nash equilibrium analysis and statistical analysis, respectively. It is also numerically shown that the proposed solutions function fairly; that is, for each type of agent, the overall workload is distributed equally.
ISSN:1076-2787
1099-0526
DOI:10.1155/2020/1752571