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| Task Allocation for Distributed Self-Interested Agents |
| FU Minglan1, WANG Hao1, FANG Baofu1, HUANG Xiaoling1,2 |
1.School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009
2.School of Computer and Information Engineering, Chuzhou University, Chuzhou 239000 |
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Abstract In the task allocation with self-interested agents, the agents cannot cooperate effectively due to their selfishness and thus their individual revenues and system performance are decreased. To make a reasonable distribution of the utilities, a self-interested agent task allocation algorithm based on the task allocation model of a distributed self-interested agent coalitional skill game is proposed. The service agents and the task agents are self-interested. They are located in different geographic locations with different scopes of vision. The utility distribution strategies are designed for task agents to make them reasonably distribute their utilities to each required skill. The task allocation results guarantee a higher system revenue even if the agents are all self-interested. The final simulation results verify the effectiveness of the proposed algorithm and examine the impacts of the scope of vision of the self-interested agents on their individual revenues and system performance.
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Received: 28 April 2018
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| Fund:Supported by Natural Science Foundation of Anhui Province(No.1708085MF146), Science and Technology Support Project of Sichuan Province(No.2016GZ0389), Project of Innovation Team of Ministry of Education of China(No.IRT17R32) |
About author:: (FU Minglan, Ph.D. candidate. Her resear-ch interests include multi-agent coordination, game theory and emotional intelligence.)
(WANG Hao(Corresponding author), Ph.D., professor. His research interests include artificial intelligence and robots.)
(FANG Baofu, Ph.D., associate professor. His research interests include machine lear-ning and machine vision.)
(HUANG Xiaoling, Ph.D. candidate, lecturer. Her research interests include natural language processing and data mining.) |
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2018, Vol. 31 
