Task allocation is a key aspect of Unmanned Aerial Vehicle(UAV)swarm collaborative operations.With an continuous increase of UAVs’scale and the complexity and uncertainty of tasks,existing methods have poor performan...Task allocation is a key aspect of Unmanned Aerial Vehicle(UAV)swarm collaborative operations.With an continuous increase of UAVs’scale and the complexity and uncertainty of tasks,existing methods have poor performance in computing efficiency,robustness,and realtime allocation,and there is a lack of theoretical analysis on the convergence and optimality of the solution.This paper presents a novel intelligent framework for distributed decision-making based on the evolutionary game theory to address task allocation for a UAV swarm system in uncertain scenarios.A task allocation model is designed with the local utility of an individual and the global utility of the system.Then,the paper analytically derives a potential function in the networked evolutionary potential game and proves that the optimal solution of the task allocation problem is a pure strategy Nash equilibrium of a finite strategy game.Additionally,a PayOff-based Time-Variant Log-linear Learning Algorithm(POTVLLA)is proposed,which includes a novel learning strategy based on payoffs for an individual and a time-dependent Boltzmann parameter.The former aims to reduce the system’s computational burden and enhance the individual’s effectiveness,while the latter can ensure that the POTVLLA converges to the optimal Nash equilibrium with a probability of one.Numerical simulation results show that the approach is optimal,robust,scalable,and fast adaptable to environmental changes,even in some realistic situations where some UAVs or tasks are likely to be lost and increased,further validating the effectiveness and superiority of the proposed framework and algorithm.展开更多
At present, robot embedded systems have some common problems such as closure and poor dynamic evolution. Aiming at resolving this situation, our paper focuses on improvements to the robot embedded system and sets up a...At present, robot embedded systems have some common problems such as closure and poor dynamic evolution. Aiming at resolving this situation, our paper focuses on improvements to the robot embedded system and sets up a new robot system architecture, and we propose a syncretic mechanism of a robot and SoftMan （SM）. In the syncretic system, the structural organization of the SoftMan group and its modes are particularly important in establishing the task coordination mechanism. This paper, therefore, proposes a coordination organization model based on the SoftMan group, and studies in detail the process of task allocation for resource contention, which facilitates a rational allocation of system resources. During our research, we introduced Resource Requirement Length Algorithm （RRLA） to calculate the resource requirements of the task and a resource conformity degree allocation algorithm of Resource Conformity Degree Algorithm （RCDA） for resource contention. Finally, a comparative evaluation of RCDA with five other frequently used task allocation algorithms shows that RCDA has higher success and accuracy rates with good stability and reliability.展开更多
Purpose-The paper aims to build the connections between game theory and the resource allocation problem with general uncertainty.It proposes modeling the distributed resource allocation problem by Bayesian game.During...Purpose-The paper aims to build the connections between game theory and the resource allocation problem with general uncertainty.It proposes modeling the distributed resource allocation problem by Bayesian game.During this paper,three basic kinds of uncertainties are discussed.Therefore,the purpose of this paper is to build the connections between game theory and the resource allocation problem with general uncertainty.Design/methodology/approach-In this paper,the Bayesian game is proposed for modeling the resource allocation problem with uncertainty.The basic game theoretical model contains three parts:agents,utility function,and decision-making process.Therefore,the probabilistic weighted Shapley value(WSV)is applied to design the utility function of the agents.For achieving the Bayesian Nash equilibrium point,the rational learning method is introduced for optimizing the decision-making process of the agents.Findings-The paper provides empirical insights about how the game theoretical model deals with the resource allocation problem uncertainty.A probabilistic WSV function was proposed to design the utility function of agents.Moreover,the rational learning was used to optimize the decision-making process of agents for achieving Bayesian Nash equilibrium point.By comparing with the models with full information,the simulation results illustrated the effectiveness of the Bayesian game theoretical methods for the resource allocation problem under uncertainty.Originality/value-This paper designs a Bayesian theoretical model for the resource allocation problem under uncertainty.The relationships between the Bayesian game and the resource allocation problem are discussed.展开更多
基金co-supported by the National Natural Science Foundation of China(Nos.71971115 and 62173305)the Postgraduate Research and Practice Innovation Program of Jiangsu Province,China(No.KYCX22_0366).
文摘Task allocation is a key aspect of Unmanned Aerial Vehicle(UAV)swarm collaborative operations.With an continuous increase of UAVs’scale and the complexity and uncertainty of tasks,existing methods have poor performance in computing efficiency,robustness,and realtime allocation,and there is a lack of theoretical analysis on the convergence and optimality of the solution.This paper presents a novel intelligent framework for distributed decision-making based on the evolutionary game theory to address task allocation for a UAV swarm system in uncertain scenarios.A task allocation model is designed with the local utility of an individual and the global utility of the system.Then,the paper analytically derives a potential function in the networked evolutionary potential game and proves that the optimal solution of the task allocation problem is a pure strategy Nash equilibrium of a finite strategy game.Additionally,a PayOff-based Time-Variant Log-linear Learning Algorithm(POTVLLA)is proposed,which includes a novel learning strategy based on payoffs for an individual and a time-dependent Boltzmann parameter.The former aims to reduce the system’s computational burden and enhance the individual’s effectiveness,while the latter can ensure that the POTVLLA converges to the optimal Nash equilibrium with a probability of one.Numerical simulation results show that the approach is optimal,robust,scalable,and fast adaptable to environmental changes,even in some realistic situations where some UAVs or tasks are likely to be lost and increased,further validating the effectiveness and superiority of the proposed framework and algorithm.
基金supported by the National Natural Science Foundation of China(No.61404069)the National High-Tech Research and Develpment(863)Program of China(No.2015AA015403)
文摘At present, robot embedded systems have some common problems such as closure and poor dynamic evolution. Aiming at resolving this situation, our paper focuses on improvements to the robot embedded system and sets up a new robot system architecture, and we propose a syncretic mechanism of a robot and SoftMan （SM）. In the syncretic system, the structural organization of the SoftMan group and its modes are particularly important in establishing the task coordination mechanism. This paper, therefore, proposes a coordination organization model based on the SoftMan group, and studies in detail the process of task allocation for resource contention, which facilitates a rational allocation of system resources. During our research, we introduced Resource Requirement Length Algorithm （RRLA） to calculate the resource requirements of the task and a resource conformity degree allocation algorithm of Resource Conformity Degree Algorithm （RCDA） for resource contention. Finally, a comparative evaluation of RCDA with five other frequently used task allocation algorithms shows that RCDA has higher success and accuracy rates with good stability and reliability.
基金supported by the Natural Science Foundation of Jiangsu Province of China(Grant No.BK20150851)the Research Innovation Program for College Graduates of Jiangsu Province(Grant No.CXLX13_09)+2 种基金funded by the China Postdoctoral Science Foundation(Grant No.2015M581842)sponsored by NUPTSF(Grant No.NY215011)funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.
文摘Purpose-The paper aims to build the connections between game theory and the resource allocation problem with general uncertainty.It proposes modeling the distributed resource allocation problem by Bayesian game.During this paper,three basic kinds of uncertainties are discussed.Therefore,the purpose of this paper is to build the connections between game theory and the resource allocation problem with general uncertainty.Design/methodology/approach-In this paper,the Bayesian game is proposed for modeling the resource allocation problem with uncertainty.The basic game theoretical model contains three parts:agents,utility function,and decision-making process.Therefore,the probabilistic weighted Shapley value(WSV)is applied to design the utility function of the agents.For achieving the Bayesian Nash equilibrium point,the rational learning method is introduced for optimizing the decision-making process of the agents.Findings-The paper provides empirical insights about how the game theoretical model deals with the resource allocation problem uncertainty.A probabilistic WSV function was proposed to design the utility function of agents.Moreover,the rational learning was used to optimize the decision-making process of agents for achieving Bayesian Nash equilibrium point.By comparing with the models with full information,the simulation results illustrated the effectiveness of the Bayesian game theoretical methods for the resource allocation problem under uncertainty.Originality/value-This paper designs a Bayesian theoretical model for the resource allocation problem under uncertainty.The relationships between the Bayesian game and the resource allocation problem are discussed.
