Discriminative Learning of First-Order Weighted Abduction from Partial Discourse Explanations
Discriminative Learning of First-Order Weighted Abduction from Partial Discourse Explanations
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DOI：
10.1007/978-3-642-37247-6_44
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发表时间：
2013-03
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影响因子：
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通讯作者：
Kazeto Yamamoto;Naoya Inoue;Yotaro Watanabe;Naoaki Okazaki;Kentaro Inui
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文献类型：
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作者：
Kazeto Yamamoto;Naoya Inoue;Yotaro Watanabe;Naoaki Okazaki;Kentaro Inui
溯因推理是对最佳解释的推论。溯因一直被广泛研究，并被广泛用于人工智能系统的建模，如诊断系统和计划识别系统。世界知识自动获取技术和推理技术的最新进展保证了将具有大知识库的溯因法应用于现实问题。然而，很少有人注意到如何自动学习评分函数，排名候选解释，他们的可解释性。在本文中，我们提出了一种新的方法，用于以监督的方式学习基于一阶逻辑的加权溯因[1]的得分函数。由于人工注释的溯因解释（即一组文字，解释意见）是一项耗时的任务，在许多情况下，我们提出了一个框架，学习分数函数部分注释溯因解释（即这些文字的子集）。更具体地说，我们假设我们将溯因应用于特定任务，其中最佳解释的子集与输出标签相关联，其余部分被视为隐藏变量。然后，我们制定的学习问题作为一个任务的歧视性结构化学习隐变量。我们的实验表明，我们的框架成功地减少了计划识别数据集上每次迭代的损失。
Abduction is inference to the best explanation. Abduction has long been studied in a wide range of contexts and is widely used for modeling artificial intelligence systems, such as diagnostic systems and plan recognition systems. Recent advances in the techniques of automatic world knowledge acquisition and inference technique warrant applying abduction with large knowledge bases to real-life problems. However, less attention has been paid to how to automatically learn score functions, which rank candidate explanations in order of their plausibility. In this paper, we propose a novel approach for learning the score function of first-order logic-based weighted abduction [1] in a supervised manner. Because the manual annotation of abductive explanations (i.e. a set of literals that explains observations) is a time-consuming task in many cases, we propose a framework to learn the score function from partially annotated abductive explanations (i.e. a subset of those literals). More specifically, we assume that we apply abduction to a specific task, where a subset of the best explanation is associated with output labels, and the rest are regarded as hidden variables. We then formulate the learning problem as a task of discriminative structured learning with hidden variables. Our experiments show that our framework successfully reduces the loss in each iteration on a plan recognition dataset.