效用最大化Agent的因果特征学习
Causal Feature Learning for Utility-Maximizing Agents
摘要：
从低层次数据中发现高层次因果关系是自然科学和社会科学中经常出现的一个重要而具有挑战性的问题。在一系列论文中，Chalupka等人（2015，2016 a，2016 b，2017）开发了一种因果特征学习（CFL）的程序，以自动化这项任务。我们认为，CFL不建议粗化的情况下，语用考虑规则有利于它，并建议粗化的情况下，语用考虑规则对it.We提出了一种新的技术，语用因果特征学习（PCFL），扩展了原来的CFL算法在有用的和直观的方式。我们表明，PCFL具有相同的吸引力的测量理论的性质，原来的CFL算法。我们通过理论分析和实验比较了这两种方法的性能。
英文摘要：
Discovering high-level causal relations from low-level data is an important and challenging problem that comes up frequently in the natural and social sciences. In a series of papers, Chalupka et al. (2015, 2016a, 2016b, 2017) develop a procedure for causal feature learning (CFL) in an effort to automate this task. We argue that CFL does not recommend coarsening in cases where pragmatic considerations rule in favor of it, and recommends coarsening in cases where pragmatic considerations rule against it. We propose a new technique, pragmatic causal feature learning (PCFL), which extends the original CFL algorithm in useful and intuitive ways. We show that PCFL has the same attractive measure-theoretic properties as the original CFL algorithm. We compare the performance of both methods through theoretical analysis and experiments.
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。
