| Presentation | 2012-11-07 Online Large-margin Weight Learning for First-order Logic-based Abduction Naoya INOUE, Kazeto YAMAMOTO, Yotaro WATANABE, Naoaki OKAZAKI, Kentaro INUI, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | 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. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Abduction / Logic-based reasoning / Online learning / Large-margin training / Structured learning / Latent variables / Passive Aggressive algorithm |
| Paper # | IBISML2012-54 |
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| Conference Information | |
| Committee | IBISML |
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| Conference Date | 2012/10/31(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| Registration To | Information-Based Induction Sciences and Machine Learning (IBISML) |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Online Large-margin Weight Learning for First-order Logic-based Abduction |
| Sub Title (in English) | |
| Keyword(1) | Abduction |
| Keyword(2) | Logic-based reasoning |
| Keyword(3) | Online learning |
| Keyword(4) | Large-margin training |
| Keyword(5) | Structured learning |
| Keyword(6) | Latent variables |
| Keyword(7) | Passive Aggressive algorithm |
| 1st Author's Name | Naoya INOUE |
| 1st Author's Affiliation | Tohoku University() |
| 2nd Author's Name | Kazeto YAMAMOTO |
| 2nd Author's Affiliation | Tohoku University |
| 3rd Author's Name | Yotaro WATANABE |
| 3rd Author's Affiliation | Tohoku University |
| 4th Author's Name | Naoaki OKAZAKI |
| 4th Author's Affiliation | Tohoku University |
| 5th Author's Name | Kentaro INUI |
| 5th Author's Affiliation | Tohoku University |
| Date | 2012-11-07 |
| Paper # | IBISML2012-54 |
| Volume (vol) | vol.112 |
| Number (no) | 279 |
| Page | pp.pp.- |
| #Pages | 8 |
| Date of Issue | |