論文

• Chiaki Sakama, Katsumi Inoue, Taisuke Sato. Linear Algebraic Characterization of Logic Programs, In: Gang Li, et al. (eds.), Knowledge Science, Engineering and Management: Proceedings of the 10th International Conference (KSEM 2017; Melbourne, VIC, Australia, August 19-20, 2017), Lecture Notes in Artificial Intelligence, Springer, Vol. 10412, pp. 520-533, Aug. 2017.
• Nicolas Schwind, Morgan Magnin, Katsumi Inoue, Tenda Okimoto, Taisuke Sato, Kazuhiro Minami, Hiroshi Maruyama. Formalization of Resilience for Constraint-Based Dynamic Systems, Journal of Reliable Intelligent Environments, Springer, Vol. 2, No. 1, pp. 17-35, Apr. 2016.
• 石畠正和, 亀谷由隆, 佐藤泰介, 湊 真一. BDD上の命題化計算に基づくEMアルゴリズム, 人工知能学会論文誌, 社団法人 人工知能学会, Vol. 25, No. 3, Feb. 2012.
• TAISUKE SATO. Variational Bayes via propositionalized probability computation in PRISM, Annals of Mathematics and Artificial Intelligence, Springer, Vol. 54, No. 1-3, pp. 135-158, Sept. 2009.
• 熊谷潤一, 小島康夫, 高重聡一, 亀谷由隆, 佐藤泰介. 頻出部分木発見手法を用いた遺伝的プログラミングの交通信号制御問題への適用, 人工知能学会論文誌, Vol. 22, No. 2, pp. 127-139, Apr. 2007.
• 三富文和, 藤原冬樹, 山本正信, 佐藤泰介. 習慣的な行動の確率文脈自由文法に基づくベイズ識別, 電子情報通信学会論文誌, Vol. J88-D-II, No. 4, pp. 716-726, May 2005.
• Zhou Neng-Fa, Sato Taisuke. Efficient Fixpoint Computation in Linear Tabling, Proceedings of the Fifth ACM-SIGPLAN International Conference on Principles and Practice of Declarative Programming (PPDP2003), pp. 275-283, 2003.
• Sato Taisuke, Zhou Neng-Fa. A New Perspective of PRISM Relational Modeling, Proceedings of IJCAI-03 workshop on Learning Statistical Models from Relational Data (SRL2003), pp. 133-139, 2003.
• Sato, T., Motomura, Y.. Toward logical-probabilistic modeling of complex systems, Proc. of the International Conference on Advances in Infrastructure for e-Business, e-Education, e-Science, and e-Medicine on the Internet, 2002.
• TAISUKE SATO. 'Parameterized Logic Programs where Computing Meets Learning (Invited), Proc. of FLOPS2001, LNCS 2024 Springer, pp. 40-60, 2001.
• Sato, T., Kameya, Y.. Parameter Learning of Logic Programs for Symbolic-statistical Modeling, Journal of Artificial Intelligence Research, Vol. 15, pp. 391-454, 2001.
• T.Sato, Y.Kameya, S.Abe, K.Shirai. A Separate-and-Learn Approach to EM Learning of PCFGs, Proc. of NLPRS2001, pp. 255-262, 2001.
• 亀谷由隆, 森高志, 佐藤泰介. WFSTに基づく確率文脈自由文法およびその拡張文法の高速EM学習法, 自然言語処理, Vol. 17, No. 6, pp. 49-84, 2001.
• 秋葉澄孝, 佐藤泰介, 元吉文男. 論理プログラムの新しい完備化と論理式の置換に基づく 計算手続きについて, 情報処理学会論文誌, Vol. 41, No. 11, pp. 3023-3036, 2000.
• Sato, T., Kameya, Y.. PRISM : A Language for Symbolic-Statistical Modeling, Proc. of Int'l Joint Conf. on AI, pp. 715-729, 1997.
• Iba, H., deGaris, H., Sato, T.. A Numerical Approach to Genetic Programing for System Identitication, Evolutionary Computation, Vol. 3, No. 4, pp. 417-452, 1996.
• TAISUKE SATO. A Statistical Learning Method for Logic Programs with Distribution Semantics, Proc. of Int'l Conf.on Logic Programming95, pp. 715-729, 1995.
• 伊庭斉志, 佐藤泰介. BUGS : A Bug-Based Search Strategy using Genetic Algorithms, JSAI, Vol. 8, No. 6, pp. 797-809, 1993.
• TAISUKE SATO. Equivalence-Preserving First Order Unfold/fold Transformation Systems, Theoretical Computer Science, Vol. 105, pp. 57-84, 1992.

著書

• 佐藤泰介, 高橋篤司, 伊東利哉, 上野修一. 情報基礎数学, オーム社, Sept. 2014.
• 佐藤泰介, 高橋篤司, 伊東利哉, 上野修一. 情報基礎数学, 昭晃堂, Oct. 2007.
• TAISUKE SATO. Statistical abduction with tabulation in Computational Logic: From Logic Programming into the Future, Springer, Springer, pp. 567-587, 2002.

国際会議発表 (査読有り)

• Masakazu ishihata, Taisuke Sato, Shin-ichi Minato. Compiling Bayesian Networks for Parameter Learning Based on Shared BDDs, The 24th Australasian Joint Conference on Artificial Intelligence, AI 2011: ADVANCES IN ARTIFICIAL INTELLIGENCE, Lecture Notes in Computer Science, Springer, Vol. 7106/2011, Page 203-212, Dec. 2011.
• Masakazu Ishihata, Taisuke Sato. Bayesian inference for statistical abduction using Markov chain Monte Carlo, The 3rd Asian Conference on Machine Learning, JMLR Workshop and Conference Proceedings, Vol. 20, Page 81–96, Nov. 2011.
• Masakazu Ishihata, Yoshitaka Kameya, Taisuke Sato, Shin-ichi Minato. Parameter learning for Bayesian networks on Shared Binary Decision Diagrams, The st International Workshop on Advanced Methodologies for Bayesian Networks, Nov. 2010.
• Masakazu ishihata, yoshitaka kameya, taisuke sato, Shin-ichi Minato. An EM Algorithm on BDDs with Order Encoding for Logic-based Probabilistic Models, The 2nd Asian Conference on Machine Learning, JMLR: Workshop and Conference Proceedings, Vol. 13, Page 161-176, Nov. 2010.
• Inoue, K., Sato, T., Ishihata, M., Kameya, Y., Nabeshima, H.. Evaluating abductive hypotheses using an EM algorithm on BDDs, The 21st International Joint Conference on Artificial Intelligence (IJCAI-2009), pp. 810–815, July 2009.
• Masakazu Ishihata, Yoshitaka Kameya, Taisuke Sato, Shin-ichi Minato. Propositionalizing the EM algorithm by BDDs, The 18th International Conference on Inductive Logic Programming, The 18th International Conference on Inductive Logic Programming: Late Breaking Papers, Sept. 2008.

国際会議発表 (査読なし・不明)

• TAISUKE SATO. Generative modeling by PRISM, The 25th International Conference on Logic Programming (ICLP-2009),, Springer, pp. 24–35, July 2009.
• Sato, T.. Inside-outside probability computation for belief propagation, Proceedings of the 20th International Joint Conference on Artificial Intelligence, pp. 2605-2610, Jan. 2007.
• Minato, S., Satoh, K., T. Sato. Compiling Bayesian networks by symbolic probability calculation based on Zero-suppressed BDDs, Proceedings of the 20th International Joint Conference on Artificial Intelligence, pp. 2550-2555, Jan. 2007.
• Kurihara, K., Kameya, Y., Sato, T.. Discovering Concepts from Word Co-occurrences with a Relational Model, Proceedings of the International Workshop on Data-Mining and Statistical Science, pp. 26-33, 2006.
• Kurihara, K., Sato, T.. Variational Bayesian Grammar Induction for Natural Language, Proceedings of the 8th International Colloquium on Grammatical Inference, pp. 84-95, 2006.
• Izumi, Y., Kameya, Y., Sato, T.. Parallel EM Learning for Symbolic-Statistical Models, Proceedings of the International Workshop on Data-Mining and Statistical Science, pp. 133-140, 2006.
• Sato, T., Kameya, Y., Zhou, N.-F.. Generative modeling with failure in PRISM, 19th International Joint Conference on Artificial Intelligence, pp. 847-852, Aug. 2005.

国内会議発表 (査読なし・不明)

• 石畠正和, 亀谷由隆, 佐藤泰介, 湊 真一. 命題論理に基づく確率モデルのための二部決定グラフと順序符号化を用いた効率的なEMアルゴリズム, 電子情報通信学会技術研究報告. IBISML, 情報論的学習理論と機械学習, 社団法人電子情報通信学会, Vol. 110, No. 76, May 2010.
• 元田剛史, 村田剛志, 佐藤泰介. 実ネットワークに対する、各種リンク予測手法の適正について, 2009年度人工知能学会全国大会（第23回）論文集, 3B1-1, 4, June 2009.
• 石畠正和, 亀谷由隆, 佐藤泰介, 湊真一. BDD上の命題化確率計算に基づくEMアルゴリズム, 人工知能学会第70回人工知能基本問題研究会, SIG-FPAI-A801, Page 15-22, Mar. 2008.
• Masanori Nakagawa, Asuka Terai, TAISUKE SATO. A Computational Model of Metaphor Understanding Using a Statistical Analysis of Japanese Corpora Based on Soft Clustering –Toward a Metaphorical Search Engine, Symposium on Large-Scale Knowledge Resources (LKR2006), Proc. of Symposium on Large-Scale Knowledge Resources (LKR2006), 27-32, Mar. 2006.

その他の論文・著書など

• 佐藤泰介, 亀谷由隆. グラフィカルモデルにおける論理的アプローチ, 人工知能学会誌, Vol. 22, No. 3, pp. 306-319, May 2007.

学位論文

• Declarative logic programming,  Thesis,  Doctor of Engineering,  Tokyo Institute of Technology,  1987/07/31,   

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