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

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
Mehdi Dadvar ◽  
Saeed Moazami ◽  
Harley R. Myler ◽  
Hassan Zargarzadeh
Keyword(s):  
Decision Making ◽  
Task Allocation ◽  
Equilibrium Analysis ◽  
Profit Margins ◽  
Dynamic Task ◽  
Dynamic Task Allocation ◽  
Game Theoretic ◽  
Simulation Results ◽  
The Stability ◽  
Theoretic Solution

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.

scholarly journals Auction-Based Consensus of Autonomous Vehicles for Multi-Target Dynamic Task Allocation and Path Planning in an Unknown Obstacle Environment

2021 ◽  
Vol 11 (11) ◽  
pp. 5057
Author(s):  
Wan-Yu Yu ◽  
Xiao-Qiang Huang ◽  
Hung-Yi Luo ◽  
Von-Wun Soo ◽  
Yung-Lung Lee
Keyword(s):  
Path Planning ◽  
Task Allocation ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Dynamic Task ◽  
The Core ◽  
Dynamic Task Allocation ◽  
Vehicle Technology ◽  
Future Work ◽  
Complicated Task

The autonomous vehicle technology has recently been developed rapidly in a wide variety of applications. However, coordinating a team of autonomous vehicles to complete missions in an unknown and changing environment has been a challenging and complicated task. We modify the consensus-based auction algorithm (CBAA) so that it can dynamically reallocate tasks among autonomous vehicles that can flexibly find a path to reach multiple dynamic targets while avoiding unexpected obstacles and staying close as a group as possible simultaneously. We propose the core algorithms and simulate with many scenarios empirically to illustrate how the proposed framework works. Specifically, we show that how autonomous vehicles could reallocate the tasks among each other in finding dynamically changing paths while certain targets may appear and disappear during the movement mission. We also discuss some challenging problems as a future work.

scholarly journals A multi-responsibility–oriented coalition formation framework for dynamic task allocation in mobile–distributed multi-agent systems

2018 ◽  
Vol 15 (6) ◽  
pp. 172988141881303 ◽  
Author(s):  
Bing Xie ◽  
Xueqiang Gu ◽  
Jing Chen ◽  
LinCheng Shen
Keyword(s):  
Coalition Formation ◽  
Task Allocation ◽  
Game Model ◽  
Multi Agent Systems ◽  
Coalition Game ◽  
Task Execution ◽  
Agent Systems ◽  
Dynamic Task ◽  
Dynamic Task Allocation ◽  
Multi Agent

In this article, we study a problem of dynamic task allocation with multiple agent responsibilities in distributed multi-agent systems. Agents in the research have two responsibilities, communication and task execution. Movements in agent task execution bring changes to the system network structure, which will affect the communication. Thus, agents need to be autonomous on communication network reconstruction for good performance on task execution. First, we analyze the relationships between the two responsibilities of agents. Then, we design a multi-responsibility–oriented coalition formation framework for dynamic task allocation with two parts, namely, task execution and self-adaptation communication. For the former part, we integrate our formerly proposed algorithm in the framework for task execution coalition formation. For the latter part, we develop a constrained Bayesian overlapping coalition game model to formulate the communication network. A task-allocation efficiency–oriented communication coalition utility function is defined to optimize a coalition structure for the constrained Bayesian overlapping coalition game model. Considering the geographical location dependence between the two responsibilities, we define constrained agent strategies to map agent strategies to potential location choices. Based on the abovementioned design, we propose a distributed location pruning self-adaptive algorithm for the constrained Bayesian overlapping coalition formation. Finally, we test the performance of our framework, multi-responsibility–oriented coalition formation framework, with simulation experiments. Experimental results demonstrate that the multi-responsibility oriented coalition formation framework performs better than the other two distributed algorithms on task completion rate (by over 9.4% and over 65% on average, respectively).

scholarly journals Analysis of Dynamic Task Allocation in Multi-Robot Systems

2006 ◽  
Author(s):  
Kristina Lerman ◽  
Chris Jones ◽  
Aram Galstyan ◽  
Maja J. Mataric
Keyword(s):  
Task Allocation ◽  
Dynamic Task ◽  
Dynamic Task Allocation ◽  
Robot Systems ◽  
Multi Robot
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