Learning Abduction Using Partial Observability
Learning Abduction Using Partial Observability
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使用部分可观察性学习溯因
DOI：
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发表时间：
2018
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影响因子：
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通讯作者：
Evan Miller
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作者：
Brendan Juba;Zongyi Li;Evan Miller
朱巴（Juba）最近提出了一种从示例中学习绑架推理的表述，其中各种解释的相对合理性以及哪些解释是有效的，直接从数据中学习。该任务的这一公式的主要缺点是，它假定访问完全信息（即完全指定）示例；相关的是，它没有为声明性背景知识提供任何作用，因为通过完整的信息在绑架任务中杂乱无章。在这项工作中，我们扩展了使用此类部分指定示例的公式，以及有关丢失数据的声明性背景知识。我们表明，有可能使用隐式学习的规则以及明确给定的声明性知识来支持假设的绑架过程。我们还展示了如何以图形因果模型的形式使用知识来完善所提出的假设。最后，我们观察到，当存在一个小的解释时，可以在充满挑战的异常耐受性环境中获得大量改良的保证。如此小的，人为理解的解释对于该任务的潜在应用特别感兴趣。
Juba recently proposed a formulation of learning abductive reasoning from examples, in which both the relative plausibility of various explanations, as well as which explanations are valid, are learned directly from data. The main shortcoming of this formulation of the task is that it assumes access to full-information (i.e., fully specified) examples; relatedly, it offers no role for declarative background knowledge, as such knowledge is rendered redundant in the abduction task by complete information. In this work we extend the formulation to utilize such partially specified examples, along with declarative background knowledge about the missing data. We show that it is possible to use implicitly learned rules together with the explicitly given declarative knowledge to support hypotheses in the course of abduction. We also show how to use knowledge in the form of graphical causal models to refine the proposed hypotheses. Finally, we observe that when a small explanation exists, it is possible to obtain a much-improved guarantee in the challenging exception-tolerant setting. Such small, human-understandable explanations are of particular interest for potential applications of the task.