Online Large-margin Weight Learning for First-order Logic-based Abduction

概要

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Abduction is inference to the best explanation. Abduction has long been studied in a wide range of contexts and is used for modeling artificial intelligence systems, such as diagnostic systems and plan recognition systems. However, less attention has been paid to how to automatically learn score functions, which rank explanations in the order of their plausibility. In this paper, we propose a supervised learning approach for first-order logic-based abduction. The contribution of this paper is the following: (i) we show how to formulate the machine learning problem of abduction with the framework of online large-margin training, which has been shown to have both predictive performance and scalability to larger problems; (ii) we extend the state-of-the-art abductive reasoning system [15] to model the score function with a weighted linear model, which is the groundwork for the online large-margin training; (iii) we support partially-specified gold-standard explanations as training examples, where the weights are learned to rank any explanation that includes the gold-standard explanation as the best explanation; (iv) the all-in-one software package for inference and learning is made publicly available.
一般社団法人電子情報通信学会の論文
2012-10-31