Learning to Optimize Autonomy in Competence-Aware Systems
Learning to Optimize Autonomy in Competence-Aware Systems
复制标题
学习优化能力感知系统中的自主性
DOI：
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复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
S. Zilberstein
中科院分区：
文献类型：
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作者：
Connor Basich;Justin Svegliato;K. H. Wray;S. Witwicki;Joydeep Biswas;S. Zilberstein
对半自治系统（SAS）的兴趣正在迅速增长，作为在偶尔需要依赖人类的领域部署自治系统的范例。这种模式允许服务机器人或自动驾驶汽车在不同程度上自主运行，并在需要人类判断的情况下提供安全保障。我们提出了一个自主的内省模型，该模型通过经验在线学习和更新，并规定了代理在任何给定情况下可以自主行动的程度。我们定义了一个能力感知系统（CAS），它在不同的自治水平和可用的人类反馈下明确地模拟自己的熟练程度。CAS学会根据经验调整其自主水平，以最大限度地提高整体效率，同时考虑到人工协助的成本。我们分析了CAS的收敛特性，并提供了机器人交付和自动驾驶领域的实验结果，证明了该方法的优点。
Interest in semi-autonomous systems (SAS) is growing rapidly as a paradigm to deploy autonomous systems in domains that require occasional reliance on humans. This paradigm allows service robots or autonomous vehicles to operate at varying levels of autonomy and offer safety in situations that require human judgment. We propose an introspective model of autonomy that is learned and updated online through experience and dictates the extent to which the agent can act autonomously in any given situation. We define a competence-aware system (CAS) that explicitly models its own proficiency at different levels of autonomy and the available human feedback. A CAS learns to adjust its level of autonomy based on experience to maximize overall efficiency, factoring in the cost of human assistance. We analyze the convergence properties of CAS and provide experimental results for robot delivery and autonomous driving domains that demonstrate the benefits of the approach.
异常恢复的信念空间元推理
DOI：
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发表时间：
2019
期刊：
IROS
影响因子：
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作者：
Svegliato, Justin;Wray, Kyle;Witwicki, Stefan;Biswas, Joydeep;Zilberstein, Shlomo
通讯作者：
Zilberstein, Shlomo
DOI：
10.24963/ijcai.2017/664
发表时间：
2017
期刊：
International Joint Conference on Artificial Intelligence
影响因子：
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作者：
Wray, Kyle Hollins;Witwicki, Stefan J.;Zilberstein, Shlomo
通讯作者：
Zilberstein, Shlomo