Taisuke Sato

Tokyo Institute of Technology, Japan
tel/fax: +81-3-5734-2186
email: sato at mi.cs.titech.ac.jp

Please visit Sato (Taisuke) Laboratory for information not found here


My research interests:
In the early eighties, I started exploring various aspects of logic programming such as program analysis, transformation and synthesis with a purpose to make logic programming more attractive as a basis for future AI. At that time I found negation in logic programs quite intriguing and studied semantics of negation together with applications to logic program synthesis. Then my interest shifted to non-deductive information processing such as genetic programming and inductive/abductive reasoning. In particular uncertainty inevitable in non-deductive reasoning strongly caught my attention. I soon began to investigate the possibility of unifying statistical inference and logical inference in a general setting. In the mid nineties, I proposed a probabilistic semantics called the distribution semantics. Since then I have been developing a probabilistic language called PRISM based on that semantics, while collaborating with the B-Prolog project by professor Zhou at the City University of New York, U.S.A. and the Lost project by Professor Christiansen at Roskilde University, Denmark. I'm also working with Professor Inoue at National Institute of Informatics for developing logical approaches to systems biology.

Recent projects:

Short CV:
I received M.S. in Electrical Engineering in 1975 and also received Ph.D. in Computer Science in 1987, both from Tokyo Institute of Technology. The title of my doctoral thesis was "Declarative Logic Programming." I joined ETL (Electrotechnical Laboratory), Japan in 1975 as a researcher and soon was involved the Fifth Generation Project in various ways. In those days, my research interest was in equivalence preserving logic program transformation and its extension to the case of general programs. In 1995 I moved to Tokyo Institute of Technology as a professor, and at the same time I changed my research direction toward non-deductive approaches to AI. In particular I concentrated on combining the idea of probabilistic logic programming and statistical learning. Later in 1997 I designed a probabilistic logic programming language PRISM. Since then I've been concentrating on the development of PRISM as a high level probabilistic modeling language. Currently I'm investigating the possibility of further generalizing PRISM by a constraint approach.

Current research topics:
Selected publications : (see Sato Lab. for a complete list of publications)

(Logic Programming)
Enumeration of Success Patterns in Logic Programs, Sato,T. and Tamaki,H.,
Theoretical Computer Science 34, 1984, pp.227-240.

Unfold/fold Transformation of Logic Programs, Tamaki,H. and Sato,T.,
Proc. 2nd Int'l Conf. on Logic Programming, Uppsala, 1984, pp.127-137.

OLD Resolution with Tabulation, Tamaki,H. and Sato,T.,
Proc. 3rd Int'l Conf. on Logic Programming, London, 1986, pp.84-98.

First Order Compiler: A Deterministic Logic Program Synthesis Algorithm, Sato,T. and Tamaki,H.,
J. Symbolic Computation 8, 1989, pp.605-627.

Completed Logic Programs and Their Consistency, Sato,T.,
J. Logic Programming 9, 1990, pp.33-44.

Equivalence-Preserving First Order Unfold/fold Transformation Systems, Sato,T.,
Theoretical Computer Science 105, 1992, pp.57-84.

Linear tabling strategies and optimization,
Zhou, N.-F., Sato, T. and Shen, Y.-D.,
Theory and Practice of Logic Programming, Vol.8, No.1, 2008, pp.81-109.

(Genetic Programming)
BUGS: A Bug-Based Search Strategy using Genetic Algorithms, Iba,H., Akiba,S., Higuchi,T. and Sato,T.,
Proc. PPSN (Parallel Problem Solving from Nature), Belgium, 1992, pp.165-174.

Evolutionary Learning Strategy using Bug-Based Search, Iba,H., Higuchi,T., de Garis,H and Sato,T.,
Proc. IJCAI'93, Shambery, 1993, pp.960-966.

A Numerical Approach to Genetic Programming for System Identification, Iba,H., deGaris,H. and Sato,T.,
Evolutionary Computation 3(4), 1996, pp.417-452.

(Statistical abduction/PRISM)
A Statistical Learning Method for Logic Programs with Distribution Semantics,
Proc. ICLP'95, 1995, pp.715-729.

PRISM:A Language for Symbolic-Statistical Modeling,
Sato,T. and Kameya,Y.,
Proc. IJCAI'97, 1997, pp.1330-1335.

Parameter Learning of Logic Programs for Symbolic-statistical Modeling,
Sato,T. and Kameya,Y.
Journal of Artificial Intelligence Research Vol.15, 2001, pp.391-454.

Variational Bayes via propositionalized probability computation in PRISM,
Sato, T., Kameya, Y., Kurihara, K.,
Annals of Mathematics and Artificial Intelligence, Vol.54, No.1-3, 2009, pp.135-158.

A general MCMC method for Bayesian inference in logic-based probabilistic modeling,
Sato, T.,
Proc. IJCAI'11, 2011, pp.1472-1477.

See also PRISM's home

(Constraint based Probabilistic Modeling)
Evaluating abductive hypotheses using an EM algorithm on BDDs,
Inoue, K., Sato, T., Ishihata, M., Kameya, Y. and Nabeshima,H.,
Proc. IJCAI'09, 2009, pp.810-815.

Constraint-based probabilistic modeling for statistical abduction,
Sato, T., Ishihata, M. and Inoue, K.,
Machine Learning, Vol.83, No.2, pp.241-264, 2011.