个人信息
参与实验室科研项目
人机混合智能系统双层智能测试评估技术研究
复杂环境下非完全信息博弈决策的智能基础模型研究
研究课题
针对不确定复杂环境下多群体博弈决策中的瓶颈问题，围绕其非完全信息、高智能、强动态的特点，从智能模型构建、多群体博弈决策理论形成以及人机对抗性能验证与评估等层面开展研究。
学术成果
共撰写/参与撰写专利 0 项，录用/发表论文 2 篇，投出待录用论文0篇。
Conference Articles
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A Human-Machine Trust Model Integrating Machine Estimated Performance
Shaojun Chen,
Yun-Bo Zhao ,
Yang Wang,
and Junsen Lu
In 2023 6th Int. Symp. Auton. Syst. ISAS
2023
[Abs]
[doi]
[pdf]
The prediction of human trust in machines within decision-aid systems is crucial for improving system performance. However, previous studies have only measured machine performance based on its decision history, failing to account for the machine’s current decision state. This delay in evaluating machine performance can result in biased trust predictions, making it challenging to enhance the overall performance of the human-machine system. To address this issue, this paper proposes incorporating machine estimated performance scores into a human-machine trust prediction model to improve trust prediction accuracy and system performance. We also provide an explanation for how this model can enhance system performance. To estimate the accuracy of the machine’s current decision, we employ the KNN method and obtain a corresponding performance score. Next, we report the estimated score to humans through the human-machine interaction interface and obtain human trust via trust self-reporting. Finally, we fit the trust prediction model parameters using data and evaluate the model’s efficacy through simulation on a public dataset. Our ablation experiments show that the model reduces trust prediction bias by 3.6% and significantly enhances the overall accuracy of humanmachine decision-making.
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Strategy Generation Based on DDPG with Prioritized Experience Replay for UCAV
Junsen Lu,
Yun-Bo Zhao ,
Yu Kang,
Yuhui Wang,
and Yimin Deng
In 2022 Int. Conf. Adv. Robot. Mechatron. ICARM
2022
[Abs]
[doi]
[pdf]
Unmanned combat aerial vehicles are becoming essential participants in future air-combat scenarios, while the optimal control strategy remains a great challenge due to the high dynamics of the aerial vehicles themselves as well as the environmental uncertainties in air-combat. Based on a deep deterministic policy gradient algorithm framework, an air combat decision-making strategy is designed and implemented, and further a prioritized experience replay method is proposed for the proposed algorithm to further improve the efficiency in the training process. Simulation experiments show that, at much reduced training cost, the proposed approach achieves superior air combat performance with fast convergence.