| Budget Amount *help |
¥42,900,000 (Direct Cost: ¥33,000,000、Indirect Cost: ¥9,900,000)
Fiscal Year 2018: ¥10,790,000 (Direct Cost: ¥8,300,000、Indirect Cost: ¥2,490,000)
Fiscal Year 2017: ¥11,050,000 (Direct Cost: ¥8,500,000、Indirect Cost: ¥2,550,000)
Fiscal Year 2016: ¥11,310,000 (Direct Cost: ¥8,700,000、Indirect Cost: ¥2,610,000)
Fiscal Year 2015: ¥9,750,000 (Direct Cost: ¥7,500,000、Indirect Cost: ¥2,250,000)
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| Outline of Final Research Achievements |
This project aimed at the computational modeling of deep, robust discourse analysis that was able to "read between the lines" by integrating abductive reasoning, machine learning, and physical computing. The main achievements are as follows. First, we significantly enhanced the computational capacity of abductive reasoning by formalizing the problem as weighted maximum satisfiability and devising efficient pruning methods. Second, we developed novel methods for large-scale knowledge acquisition from both Web and Wikipedia documents and demonstrated the impact of leveraging acquired knowledge on semantic and discourse analysis. Third, we built and empirically evaluated a computational model that innovatively integrated abductive reasoning and physical simulation to predict risks involved in given traffic scenes. The resources and tools developed in this project are made publicly available on our website.
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