Predictive Control Parser: Extended LINGOL. - Hozumi Tanaka; Taisuke Sato; Fumio Motoyoshi (IJCAI, 1979)

SYSP: A New Programming Language for the Next Generation. - Toshio Yokoi; Shooichi Yokoyama; Taisuke Sato; Fumio Motoyoshi; Kazuhiro Fuchi (IJCAI, 1979)

SGS: A System For Mechanical Generation Of Japanese Sentences. - Taisuke Sato (COLING, 1980)

Negation and Semantics of Prolog Programs. - Taisuke Sato (ICLP, 1982)

An Algorithm for Intelligent Backtracking. - Taisuke Sato (RIMS Symposium on Software Science and Engineering, 1982)

Program Transformation Through Meta-shifting. - Hisao Tamaki; Taisuke Sato (New Gener. Comput., 1983)

Transformational Logic Program Synthesis. - Taisuke Sato; Hisao Tamaki (FGCS, 1984)

Unfold/Fold Transformation of Logic Programs. - Hisao Tamaki; Taisuke Sato (ICLP, 1984)

Enumeration of Success Patterns in Logic Programs. - Taisuke Sato; Hisao Tamaki (Theor. Comput. Sci., 1984)

OLD Resolution with Tabulation. - Hisao Tamaki; Taisuke Sato (ICLP, 1986)

First Order Compiler: A Deterministic Logic Program Synthesis Algorithm. - Taisuke Sato; Hisao Tamaki (J. Symb. Comput., 1989)

Existential Continuation. - Taisuke Sato; Hisao Tamaki (New Gener. Comput., 1989)

An Equivalence Preserving First Order Unfold/fold Transformation System. - Taisuke Sato (ALP, 1990)

Completed Logic Programs and their Consistency. - Taisuke Sato (J. Log. Program., 1990)

Full First Order Logic Programming and Truth Predicate. - Taisuke Sato (ICLP, 1991)

A Complete Top-Down Interpreter for First Order Programs. - Taisuke Sato; Fumio Motoyoshi (ISLP, 1991)

Meta-Programming through a Truth Predicate. - Taisuke Sato (JICSLP, 1992)

BUGS: A Bug-Based Search Strategy using Genetic Algorithms. - Hitoshi Iba; Sumitaka Akiba; Tetsuya Higuchi; Taisuke Sato (PPSN, 1992)

Equivalence-Preserving First-Order Unfold/Fold Transformation Systems. - Taisuke Sato (Theor. Comput. Sci., 1992)

Inductive Resolution. - Taisuke Sato; Sumitaka Akiba (ALT, 1993)

System Identification using Structured Genetic Algorithms. - Hitoshi Iba; Takio Kurita; Hugo de Garis; Taisuke Sato (ICGA, 1993)

Evolutionary Learning Strategy using Bug-Based Search. - Hitoshi Iba; Tetsuya Higuchi; Hugo de Garis; Taisuke Sato (IJCAI, 1993)

Learning Logic Programs and Regularities from Examples by Inductive Inference. - Sumitaka Akiba; Taisuke Sato (Machine Intelligence 14, 1993)

System Identification Approach to Genetic Programming. - Hitoshi Iba; Taisuke Sato; Hugo de Garis (International Conference on Evolutionary Computation, 1994)

Genetic Programming with Local Hill-Climbing. - Hitoshi Iba; Hugo de Garis; Taisuke Sato (PPSN, 1994)

Temporal Data Processing Using Genetic Programming. - Hitoshi Iba; Taisuke Sato; Hugo de Garis (ICGA, 1995)

A Statistical Learning Method for Logic Programs with Distribution Semantics. - Taisuke Sato (ICLP, 1995)

A Numerical Approach to Genetic Programming for System Identification. - Hitoshi Iba; Hugo de Garis; Taisuke Sato (Evol. Comput., 1995)

PRISM: A Language for Symbolic-Statistical Modeling. - Taisuke Sato; Yoshitaka Kameya (IJCAI, 1997)

Abstracting a Human's Decision Process by PRISM. - Yoshitaka Kameya; Taisuke Sato (Discovery Science, 1998)

A Graphical Method for Parameter Learning of Symbolic-Statistical Models. - Yoshitaka Kameya; Nobuhisa Ueda; Taisuke Sato (Discovery Science, 1999)

Reactive Logic Programming by Reinforcement Learning. - Taisuke Sato; Satoshi Funada (ICLP, 1999)

Efficient EM Learning with Tabulation for Parameterized Logic Programs. - Yoshitaka Kameya; Taisuke Sato (Computational Logic, 2000)

Simplified Training Algorithms for Hierarchical Hidden Markov Models. - Nobuhisa Ueda; Taisuke Sato (Discovery Science, 2001)

Parameterized Logic Programs where Computing Meets Learning. - Taisuke Sato (FLOPS, 2001)

A Separate-and-Learn Approach to EM Learning of PCFGs. - Taisuke Sato; Shigeru Abe; Yoshitaka Kameya; Kiyoaki Shirai (NLPRS, 2001)

Parameter Learning of Logic Programs for Symbolic-Statistical Modeling. - Taisuke Sato; Yoshitaka Kameya (J. Artif. Intell. Res., 2001)

Tutorial Series on Web-computing - Preface. - Masayuki Numao; Taisuke Sato (New Gener. Comput., 2001)

Statistical Abduction with Tabulation. - Taisuke Sato; Yoshitaka Kameya (Computational Logic: Logic Programming and Beyond, 2002)

EM Learning for Symbolic-Statistical Models in Statistical Abduction. - Taisuke Sato (Progress in Discovery Science, 2002)

Efficient fixpoint computation in linear tabling. - Neng-Fa Zhou; Taisuke Sato (PPDP, 2003)

Yet More Efficient EM Learning for Parameterized Logic Programs by Inter-Goal Sharing. - Yoshitaka Kameya; Taisuke Sato; Neng-Fa Zhou (ECAI, 2004)

Negation Elimination for Finite PCFGs. - Taisuke Sato; Yoshitaka Kameya (LOPSTR, 2004)

Semi-naive evaluation in linear tabling. - Neng-Fa Zhou; Yi-Dong Shen; Taisuke Sato (PPDP, 2004)

Learning through failure. - Taisuke Sato; Yoshitaka Kameya (Probabilistic, Logical and Relational Learning, 2005)

Generative Modeling with Failure in PRISM. - Taisuke Sato; Yoshitaka Kameya; Neng-Fa Zhou (IJCAI, 2005)

Bayesian Classification of Task-Oriented Actions Based on Stochastic Context-Free Grammar. - Masanobu Yamamoto; Humikazu Mitomi; Fuyuki Fujiwara; Taisuke Sato (FGR, 2006)

Variational Bayesian Grammar Induction for Natural Language. - Kenichi Kurihara; Taisuke Sato (ICGI, 2006)

Variational Bayes via Propositionalization. - Taisuke Sato; Yoshitaka Kameya; Kenichi Kurihara (Probabilistic, Logical and Relational Learning - A Further Synthesis, 2007)

Compiling Bayesian Networks by Symbolic Probability Calculation Based on Zero-Suppressed BDDs. - Shin-ichi Minato; Ken Satoh; Taisuke Sato (IJCAI, 2007)

Inside-Outside Probability Computation for Belief Propagation. - Taisuke Sato (IJCAI, 2007)

Discovering Concepts from Word Co-occurrences with a Relational Model. - Kenichi Kurihara; Yoshitaka Kameya; Taisuke Sato (Inf. Media Technol., 2007)

Bayesian classification of a human custom based on stochastic context-free grammar. - Humikazu Mitomi; Fuyuki Fujiwara; Masanobu Yamamoto; Taisuke Sato (Syst. Comput. Jpn., 2007)

New Advances in Logic-Based Probabilistic Modeling by PRISM. - Taisuke Sato; Yoshitaka Kameya (Probabilistic Inductive Logic Programming, 2008)

Identification of MCMC Samples for Clustering. - Kenichi Kurihara; Tsuyoshi Murata; Taisuke Sato (LKR, 2008)

Variational Bayes via propositionalized probability computation in PRISM. - Taisuke Sato; Yoshitaka Kameya; Kenichi Kurihara (Ann. Math. Artif. Intell., 2008)

A glimpse of symbolic-statistical modeling by PRISM. - Taisuke Sato (J. Intell. Inf. Syst., 2008)

Linear tabling strategies and optimizations. - Neng-Fa Zhou; Taisuke Sato; Yi-Dong Shen (Theory Pract. Log. Program., 2008)

Generative Modeling by PRISM. - Taisuke Sato (ICLP, 2009)

Evaluating Abductive Hypotheses using an EM Algorithm on BDDs. - Katsumi Inoue; Taisuke Sato; Masakazu Ishihata; Yoshitaka Kameya; Hidetomo Nabeshima (IJCAI, 2009)

Statistical Learning of Probabilistic BDDs. - Taisuke Sato (SAGA, 2009)

Logic-Based Probabilistic Modeling. - Taisuke Sato (WoLLIC, 2009)

Mode-Directed Tabling for Dynamic Programming, Machine Learning, and Constraint Solving. - Neng-Fa Zhou; Yoshitaka Kameya; Taisuke Sato (ICTAI (2), 2010)

An EM Algorithm on BDDs with Order Encoding for Logic-based Probabilistic Models. - Masakazu Ishihata; Yoshitaka Kameya; Taisuke Sato; Shin-ichi Minato (ACML, 2010)

CHR(PRISM)-based probabilistic logic learning. - Jon Sneyers; Wannes Meert; Joost Vennekens; Yoshitaka Kameya; Taisuke Sato (Theory Pract. Log. Program., 2010)

Compiling Bayesian Networks for Parameter Learning Based on Shared BDDs. - Masakazu Ishihata; Taisuke Sato; Shin-ichi Minato (Australasian Conference on Artificial Intelligence, 2011)

Kinetic Models and Qualitative Abstraction for Relational Learning in Systems Biology. - Gabriel Synnaeve; Katsumi Inoue; Andrei Doncescu; Hidetomo Nabeshima; Yoshitaka Kameya; Masakazu Ishihata; Taisuke Sato (BIOINFORMATICS, 2011)

Discretized Kinetic Models for Abductive Reasoning in Systems Biology. - Gabriel Synnaeve; Katsumi Inoue; Andrei Doncescu; Hidetomo Nabeshima; Yoshitaka Kameya; Masakazu Ishihata; Taisuke Sato (BIOSTEC (Selected Papers), 2011)

Verbal Characterization of Probabilistic Clusters Using Minimal Discriminative Propositions. - Yoshitaka Kameya; Satoru Nakamura; Tatsuya Iwasaki; Taisuke Sato (ICTAI, 2011)

A General MCMC Method for Bayesian Inference in Logic-Based Probabilistic Modeling. - Taisuke Sato (IJCAI, 2011)

Variational Bayes Inference for Logic-Based Probabilistic Models on BDDs. - Masakazu Ishihata; Yoshitaka Kameya; Taisuke Sato (ILP, 2011)

Bayesian inference for statistical abduction using Markov chain Monte Carlo. - Masakazu Ishihata; Taisuke Sato (ACML, 2011)

Constraint-based probabilistic modeling for statistical abduction. - Taisuke Sato; Masakazu Ishihata; Katsumi Inoue (Mach. Learn., 2011)

Tabling for infinite probability computation. - Taisuke Sato; Philipp J. Meyer (ICLP (Technical Communications), 2012)

RP-growth: Top-k Mining of Relevant Patterns with Minimum Support Raising. - Yoshitaka Kameya; Taisuke Sato (SDM, 2012)

Goal and Plan Recognition via Parse Trees Using Prefix and Infix Probability Computation. - Ryosuke Kojima; Taisuke Sato (ILP, 2014)

A Logic-based Approach to Generatively Defined Discriminative Modeling. - Taisuke Sato; Keiichi Kubota; Yoshitaka Kameya (CoRR, 2014)

Using WFSTs for Efficient EM Learning of Probabilistic CFGs and Their Extensions. - Yoshitaka Kameya; Takashi Mori; Taisuke Sato (Inf. Media Technol., 2014)

Infinite probability computation by cyclic explanation graphs. - Taisuke Sato; Philipp J. Meyer (Theory Pract. Log. Program., 2014)

Boxcan: A platform realizing fast retrieval of parent-child tree of containers and inner objects over EPCIS events. - Yuki Sato; Taisuke Sato; Jin Mitsugi (APCC, 2015)

Introduction to the special issue on probability, logic and learning. - James Cussens; Luc De Raedt; Angelika Kimmig; Taisuke Sato (Theory Pract. Log. Program., 2015)

Viterbi training in PRISM. - Taisuke Sato; Keiichi Kubota (Theory Pract. Log. Program., 2015)

Formalization of resilience for constraint-based dynamic systems. - Nicolas Schwind; Morgan Magnin; Katsumi Inoue; Tenda Okimoto; Taisuke Sato; Kazuhiro Minami; Hiroshi Maruyama (J. Reliab. Intell. Environ., 2016)

Embedding Tarskian Semantics in Vector Spaces. - Taisuke Sato (AAAI Workshops, 2017)

Learning probability by comparison. - Taisuke Sato (AMBN, 2017)

Linear Algebraic Characterization of Logic Programs. - Chiaki Sakama; Katsumi Inoue; Taisuke Sato (KSEM, 2017)

A linear algebraic approach to datalog evaluation. - Taisuke Sato (Theory Pract. Log. Program., 2017)

Abducing Relations in Continuous Spaces. - Taisuke Sato; Katsumi Inoue; Chiaki Sakama (IJCAI, 2018)

Computing Logic Programming Semantics in Linear Algebra. - Hien D. Nguyen 002; Chiaki Sakama; Taisuke Sato; Katsumi Inoue (MIWAI, 2018)

Partial Evaluation of Logic Programs in Vector Spaces. - Chiaki Sakama; Hien D. Nguyen 002; Taisuke Sato; Katsumi Inoue (CoRR, 2018)

Learning to rank in PRISM. - Ryosuke Kojima; Taisuke Sato (Int. J. Approx. Reason., 2018)

Logical Inference as Cost Minimization in Vector Spaces. - Taisuke Sato; Ryosuke Kojima (IJCAI, 2019)

T-PRISM: A tensorized logic programming language for data modelling. - Ryosuke Kojima; Taisuke Sato (NeSy@IJCAI, 2019)

A tensorized logic programming language for large-scale data. - Ryosuke Kojima; Taisuke Sato (CoRR, 2019)

From 3-valued Semantics to Supported Model Computation for Logic Programs in Vector Spaces. - Taisuke Sato; Chiaki Sakama; Katsumi Inoue (ICAART (2), 2020)

Boolean Network Learning in Vector Spaces for Genome-wide Network Analysis. - Taisuke Sato; Ryosuke Kojima (KR, 2021)

Logic programming in tensor spaces. - Chiaki Sakama; Katsumi Inoue; Taisuke Sato (Ann. Math. Artif. Intell., 2021)

MatSat: a matrix-based differentiable SAT solver. - Taisuke Sato; Ryosuke Kojima (CoRR, 2021)

An efficient reasoning method on logic programming using partial evaluation in vector spaces. - Hien D. Nguyen 002; Chiaki Sakama; Taisuke Sato; Katsumi Inoue (J. Log. Comput., 2021)

Pseudo-Laminarization of Mixed Microbubble Water and Complex Fluids in Capillary Flows. - Takaki Kobayashi; Akiomi Ushida; Taisuke Sato (Symmetry, 2021)

Towards end-to-end ASP computation. - Taisuke Sato; Akihiro Takemura; Katsumi Inoue (CoRR, 2023)

Differentiable learning of matricized DNFs and its application to Boolean networks. - Taisuke Sato; Katsumi Inoue (Mach. Learn., 2023)