Modelling Experts' Sampling Strategy to Balance Multiple Objectives During Scientific Explorations
Modelling Experts' Sampling Strategy to Balance Multiple Objectives During Scientific Explorations
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DOI：
10.1145/3610977.3635112
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发表时间：
2024-03
期刊：
影响因子：
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通讯作者：
Shipeng Liu;Cristina G. Wilson;Zachary I. Lee;Feifei Qian
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文献类型：
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作者：
Shipeng Liu;Cristina G. Wilson;Zachary I. Lee;Feifei Qian
我们对人类采样决策数据的分析表明，科学家根据两个关键因素调整采样策略来平衡多个目标：当前环境信息水平，以及具有巨大潜在回报的采样位置选项的可用性。虽然这项工作只是开发认知兼容的机器人决策算法的一个开始，但我们的研究结果表明，通过更好地理解人类决策过程，机器人可以使用极其简单的算法将专家的高级目标连接到所需的采样位置，同时平衡多个目标。展望未来，探索人类如何在更复杂的科学探索场景下协调和优先考虑多个目标，例如多个相互竞争的假设、有关多个变量的假设或额外的采样目标，将有助于探索。这些理解可以帮助我们的机器人制定可解释的采样策略，这些策略与人类的高水平目标非常一致，并提高人类在团队合作过程中的信任和信心。这些认知理解还可以让机器人识别人类决策中的潜在漏洞，例如偏见和疲劳，并提供有针对性的支持以提高科学成果。此外，我们期望这些认知洞察可以通过告知使用哪些算法来补充现有的机器人决策方法，并最终使机器人成为能够真正参与决策过程的智能队友。
Our analysis of human sampling decision data reveals that scientists adapt their sampling strategies to balance multiple objectives based on two key factors: the current level of information about the environment, and the availability of sampling location options with large potential rewards. While this work is only a beginning step towards the development of cognitive-compatible robotic decision algorithms, our findings show by better understanding human decision processes, robots can use extremely simple algorithms to connect experts' high-level objectives to desired sampling locations while balancing multiple objectives. Going forward, exploring how humans coordinate and prioritize multiple objectives under more sophisticated scientific exploration scenarios, such as with multiple competing hypotheses, with hypotheses regarding multiple variables, or with additional sampling objectives, would be helpful to explore. These understandings could help our robots produce explainable sampling strategies that are well-aligned with humans' high level goals, and improve humans' trust and confidence during teaming. These cognitive understandings could also allow robots to identify potential vulnerabilities in human decisions, such as biases and fatigue, and provide targeted support to enhance scientific outcomes. In addition, we expect that these cognitive insights could complement existing robotic decision methods by informing which algorithms to use, and eventually empower robots to become intelligent teammates that can truly participate in the decision-making process.