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- W4200521339 abstract "Crowdtesting is one of the hot spots in artificial intelligence in recent years. Among which multi-agent crowdtesting system is a method to deal with the complex problems in crowdtesting process. How to improve the efficiency on the premise of ensuring the robustness of the system has become an urgent issue. This paper takes the task assignment in the crowdtesting process as the research background. On the basis of a single agent, this paper proposes a multi-agent collaboration framework and designs imperfect-information sharing mechanism by combining with Q-Learning and finally we optimized the algorithm. We performed relevant simulation experiments. Compared with traditional machine learning algorithms in indicators such as robustness and adaptability, our algorithm—Q-Learning model based on imperfect-information under multiagent crowdtesting (QMIMC) had a good performance. In addition, this paper provides a certain reference for the application of multi-agent systems in crowdtesting systems." @default.
- W4200521339 created "2021-12-31" @default.
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- W4200521339 date "2021-09-01" @default.
- W4200521339 modified "2023-09-24" @default.
- W4200521339 title "QMIMC: Q-Learning Model Based on Imperfect-information under Multi-agent Crowdtesting" @default.
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- W4200521339 doi "https://doi.org/10.1109/ispa-bdcloud-socialcom-sustaincom52081.2021.00153" @default.
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