机器人倒水：使用概率实际因果识别溢出原因并选择替代动作参数
Robot Pouring: Identifying Causes of Spillage and Selecting Alternative Action Parameters Using Probabilistic Actual Causation
AI总结：
本文研究使用概率实际因果来识别机器人倒水任务中溢出的原因，并基于因果图和条件概率分布选择替代动作参数以避免溢出，验证了其实际应用价值。
摘要：
在日常生活中，我们执行涉及大量不同物体和目标的任务（例如烹饪或清洁）。当面对意外或不需要的结果时，我们会采取纠正措施并再次尝试，直到达到期望的结果。为了识别观察到的结果的原因并选择适当的纠正措施而进行的推理，是人类推理中成功执行任务的关键方面。这种推理的核心是假设某个因素负责产生观察到的结果。在本文中，我们研究使用概率实际因果来确定一个因素是否是观察到的非期望结果的原因。此外，我们展示了如何使用实际因果概率来寻找改变结果的替代动作。我们将概率实际因果分析应用于机器人倒水任务。当发生溢出时，分析表明某个任务参数是否是原因以及应如何更改以避免溢出。该分析需要任务的因果图和相应的条件概率分布。为了满足这些要求，我们使用来自机器人倒水任务真实模拟的数据执行完整的因果建模程序（即任务分析、变量定义、因果图结构确定和条件概率分布估计），涵盖了任务参数的大型组合空间。基于结果，我们讨论了变量表示的含义，以及实际因果分析建议的替代动作如何与人类观察者提出的替代解决方案进行比较。展示了概率实际因果分析在选择替代动作参数中的实际应用。
英文摘要：
In everyday life, we perform tasks (e.g., cooking or cleaning) that involve a large variety of objects and goals. When confronted with an unexpected or unwanted outcome, we take corrective actions and try again until achieving the desired result. The reasoning performed to identify a cause of the observed outcome and to select an appropriate corrective action is a crucial aspect of human reasoning for successful task execution. Central to this reasoning is the assumption that a factor is responsible for producing the observed outcome. In this paper, we investigate the use of probabilistic actual causation to determine whether a factor is the cause of an observed undesired outcome. Furthermore, we show how the actual causation probabilities can be used to find alternative actions to change the outcome. We apply the probabilistic actual causation analysis to a robot pouring task. When spillage occurs, the analysis indicates whether a task parameter is the cause and how it should be changed to avoid spillage. The analysis requires a causal graph of the task and the corresponding conditional probability distributions. To fulfill these requirements, we perform a complete causal modeling procedure (i.e., task analysis, definition of variables, determination of the causal graph structure, and estimation of conditional probability distributions) using data from a realistic simulation of the robot pouring task, covering a large combinatorial space of task parameters. Based on the results, we discuss the implications of the variables' representation and how the alternative actions suggested by the actual causation analysis would compare to the alternative solutions proposed by a human observer. The practical use of the analysis of probabilistic actual causation to select alternative action parameters is demonstrated.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
