Knowledge description and semantics in non-deductive reasoning

Desire to mechanize non-deductive reasoning has resulted some fruits in the field of artificial intelligence. On induction, classification rule learning systems have been widely studied. On analogical reasoning, a problem solver that uses a similar experience in the past was proposed. On abduction, an efficient mechanism to manage consistency of sets of hypotheses is developed. However the past sudies also have problems. The first issue is on knowledge description. A way to describe a task of non-deductive reasoning system can hugely influence effectiveness of the system. The second issue is on semantics. A uique application of abduction on logic program is seldom discussed, though its semantics is widely studied. In analogical reasoning, a desire to implement practial systems has somehow postponed analyzing semantic nature of analogical reasoning. The thesis attempts to propose solutions for the problems. For the first issue, preprocess system or a subsystem that can change the description of the task is given for the main reasoning system. For the second issue, analogical reasoning is regarded as an application of abduction and its declarative semantics is given. The main outcomes of the thesis are as follows. ･･･Thesis (Ph. D. in Engineering)--University of Tsukuba, (B), no. 1547, 1999.7.23Bibliography: p. 97-102Titlepage, abstract -- Contents -- List of figures -- Chapter 1. Introduction -- Chapter 2. On induction -- Chapter 3. On analogical reasoning -- Chapter 4. Creativity support system -- Chapter 5. On abduction -- Chapter 6. Conclusion -- Acknowledgments -- Bibliograph

Computed tomography image reconstruction using stacked U-Net

Since the development of deep learning methods, many researchers have focused on image quality improvement using convolutional neural networks. They proved its effectivity in noise reduction, single-image super-resolution, and segmentation. In this study, we apply stacked U-Net, a deep learning method, for X-ray computed tomography image reconstruction to generate high-quality images in a short time with a small number of projections. It is not easy to create highly accurate models because medical images have few training images due to patients’ privacy issues. Thus, we utilize various images from the ImageNet, a widely known visual database. Results show that a cross-sectional image with a peak signal-to-noise ratio of 27.93 db and a structural similarity of 0.886 is recovered for a 512*512 image using 360-degree rotation, 512 detectors, and 64 projections, with a processing time of 0.11 s on the GPU. Therefore, the proposed method has a shorter reconstruction time and better image quality than the existing methods

サッカーPK戦におけるゲーム理論上の最適戦略とプロの戦略との差異に関する考察

人や企業は様々な条件下で最適な行動を取るのだろうか．取らないのであればそれはなぜか．その原因を求めることは，実際の個人・企業等の理解を大きく助ける．また，ゲーム理論はスポーツや経済学そしてその他の社会科学の理解に大きく関わってきた．本研究は比較的データが集めやすく混合戦略を適用できるサッカーのPK戦に注目し，独自の確率を考慮した利得表を作成した．その利得表を用いてPK戦におけるキッカーの最適戦略を求め，最適戦略と実際の戦略とのズレを明らかにした．そのズレの原因を求める為にデータセット内の各データ項目についての確率分布を比較するというアプローチをした．データはインターネット動画サイトより収集した，プロ選手による2001年〜2017年の間の世界各国のPK戦150試合（計1539人分）を使用した．実験結果として，最適戦略と実際の戦略との間にズレが存在することが分かった．またそのズレには国籍・スコア差の関与が示唆された．その結果から，サッカーPK戦における最適戦略と実際の戦略との間におけるズレの原因を推定した．本手法はスポーツ分野以外への応用も期待できる