Article

BLOG: probabilistic models with unknown objects

Authors:

Bhaskara Marthi,

Stuart Russell,

Andrey KolobovAuthors Info & Claims

IJCAI'05: Proceedings of the 19th international joint conference on Artificial intelligence

Pages 1352 - 1359

Published: 30 July 2005 Publication History

Abstract

This paper introduces and illustrates BLOG, a formal language for defining probability models over worlds with unknown objects and identity uncertainty. BLOG unifies and extends several existing approaches. Subject to certain acyclicity constraints, every BLOG model specifies a unique probability distribution over first-order model structures that can contain varying and unbounded numbers of objects. Furthermore, complete inference algorithms exist for a large fragment of the language. We also introduce a probabilistic form of Skolemization for handling evidence.

References

[1]

{Charniak and Goldman, 1993} E. Charniak and R. P. Goldman. A Bayesian model of plan recognition. AIJ, 64(1):53-79, 1993.

Digital Library

[2]

{Domingos and Richardson, 2004} P. Domingos and M. Richardson. Markov logic: A unifying framework for statistical relational learning. In Proc. ICML Wksp on Statistical Relational Learning and Its Connections to Other Fields, 2004.

[3]

{Friedman et al., 1999} N. Friedman, L. Getoor, D. Koller, and A. Pfeffer. Learning probabilistic relational models. In Proc. 16th IJCAI, pages 1300-1307, 1999.

Digital Library

[4]

{Gaifman, 1964} H. Gaifman. Concerning measures in first order calculi. Israel J. Math., 2:1-18, 1964.

[5]

{Getoor et al., 2001} L. Getoor, N. Friedman, D. Koller, and B. Taskar. Learning probabilistic models of relational structure. In Proc. 18th ICML, pages 170-177, 2001.

Digital Library

[6]

{Gilks et al., 1994} W. R. Gilks, A. Thomas, and D. J. Spiegelhalter. A language and program for complex Bayesian modelling. The Statistician, 43(1):169-177, 1994.

[7]

{Halpern, 1990} J. Y. Halpern. An analysis of first-order logics of probability. AIJ, 46:311-350, 1990.

Digital Library

[8]

{Heckerman et al., 2004} D. Heckerman, C. Meek, and D. Koller. Probabilistic models for relational data. Technical Report MSRTR- 2004-30, Microsoft Research, 2004.

[9]

{Jaeger, 2001} M. Jaeger. Complex probabilistic modeling with recursive relational Bayesian networks. Annals of Math and AI, 32:179-220, 2001.

Digital Library

[10]

{Kersting and De Raedt, 2001} K. Kersting and L. De Raedt. Adaptive Bayesian logic programs. In 11th Int. Conf. on ILP, 2001.

Digital Library

[11]

{Koller and Pfeffer, 1998} D. Koller and A. Pfeffer. Probabilistic frame-based systems. In Proc. 15th AAAI, pages 580-587, 1998.

Digital Library

[12]

{Laskey, 2004} K. B. Laskey. MEBN: A logic for open-world probabilistic reasoning. Technical report, George Mason Univ., 2004.

[13]

{Milch et al., 2005} B. Milch, B. Marthi, D. Sontag, S. Russell, D. L. Ong, and A. Kolobov. Approximate inference for infinite contingent Bayesian networks. In 10th AISTATS Wksp, 2005.

[14]

{Mjolsness, 2004} E. Mjolsness. Labeled graph notations for graphical models. Technical Report 04-03, School of Information and Computer Science, UC Irvine, 2004.

[15]

{Ng and Subrahmanian, 1992} R. T. Ng and V. S. Subrahmanian. Probabilistic logic programming. Information and Computation, 101(2):150-201, 1992.

Digital Library

[16]

{Pasula et al., 2003} H. Pasula, B. Marthi, B. Milch, S. Russell, and I. Shpitser. Identity uncertainty and citation matching. In NIPS 15. MIT Press, Cambridge, MA, 2003.

[17]

{Pearl, 1988} J. Pearl. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Francisco, revised edition, 1988.

Digital Library

[18]

{Pfeffer, 2001} A. Pfeffer. IBAL: A probabilistic rational programming language. In Proc. 17th IJCAI, pages 733-740, 2001.

Digital Library

[19]

{Poole, 1993} D. Poole. Probabilistic Horn abduction and Bayesian networks. AIJ, 64(1):81-129, 1993.

Digital Library

[20]

{Sato and Kameya, 2001} T. Sato and Y. Kameya. Parameter learning of logic programs for symbolic-statistical modeling. JAIR, 15:391-454, 2001.

Digital Library

[21]

{Sittler, 1964} R. W. Sittler. An optimal data association problem in surveillance theory. IEEE Trans. Military Electronics, MIL- 8:125-139, 1964.

Cited By

Molino PRé C(2021)Declarative Machine Learning SystemsQueue10.1145/3475965.347931519:3(46-76)Online publication date: 3-Aug-2021
https://dl.acm.org/doi/10.1145/3475965.3479315
Borgwardt SCeylan İLukasiewicz T(2019)Ontology-mediated query answering over log-linear probabilistic dataProceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v33i01.33012711(2711-2718)Online publication date: 27-Jan-2019
https://dl.acm.org/doi/10.1609/aaai.v33i01.33012711
Walia RNarayanan PCarette JTobin-Hochstadt SShan C(2019)From high-level inference algorithms to efficient codeProceedings of the ACM on Programming Languages10.1145/33417023:ICFP(1-30)Online publication date: 26-Jul-2019
https://dl.acm.org/doi/10.1145/3341702
Show More Cited By

Recommendations

Automatic inference in BLOG
AAAIWS'10-06: Proceedings of the 6th AAAI Conference on Statistical Relational Artificial Intelligence

BLOG is a powerful language to express models with an unknown number of objects and identity uncertainty. Current inference engines for BLOG are either too slow or require users to write a model-specific proposal distribution. We describe here, ongoing ...
Rule Extraction from Blog Using Inductive Logic Programming
WI-IAT '10: Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03

Information recommender system attempts to present information that is likely to be useful for the user. Showing recommendation reason is an important role of the system. However, current recommender systems give only simple or quantitative reasons for ...
Intuitionistic Trilattice Logics

We take up a suggestion by Odintsov (2009, Studia Logica, 91, 407–428) and define intuitionistic variants of certain logics arising from the trilattice SIXTEEN₃ introduced in Shramko and Wansing (2005, Journal of Philosophical Logic, 34, 121–153 and ...

Comments

Information & Contributors

Information

Published In

cover image Guide Proceedings

IJCAI'05: Proceedings of the 19th international joint conference on Artificial intelligence

July 2005

1775 pages

Sponsors

The International Joint Conferences on Artificial Intelligence, Inc.

Publisher

Morgan Kaufmann Publishers Inc.

San Francisco, CA, United States

Publication History

Published: 30 July 2005

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

77
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 05 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Molino PRé C(2021)Declarative Machine Learning SystemsQueue10.1145/3475965.347931519:3(46-76)Online publication date: 3-Aug-2021
https://dl.acm.org/doi/10.1145/3475965.3479315
Borgwardt SCeylan İLukasiewicz T(2019)Ontology-mediated query answering over log-linear probabilistic dataProceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v33i01.33012711(2711-2718)Online publication date: 27-Jan-2019
https://dl.acm.org/doi/10.1609/aaai.v33i01.33012711
Walia RNarayanan PCarette JTobin-Hochstadt SShan C(2019)From high-level inference algorithms to efficient codeProceedings of the ACM on Programming Languages10.1145/33417023:ICFP(1-30)Online publication date: 26-Jul-2019
https://dl.acm.org/doi/10.1145/3341702
Cusumano-Towner MSaad FLew AMansinghka VMcKinley KFisher K(2019)Gen: a general-purpose probabilistic programming system with programmable inferenceProceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation10.1145/3314221.3314642(221-236)Online publication date: 8-Jun-2019
https://dl.acm.org/doi/10.1145/3314221.3314642
Grohe MLindner PSuciu DSkritek SKoch C(2019)Probabilistic Databases with an Infinite Open-World AssumptionProceedings of the 38th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems10.1145/3294052.3319681(17-31)Online publication date: 25-Jun-2019
https://dl.acm.org/doi/10.1145/3294052.3319681
Saad FCusumano-Towner MSchaechtle URinard MMansinghka V(2019)Bayesian synthesis of probabilistic programs for automatic data modelingProceedings of the ACM on Programming Languages10.1145/32903503:POPL(1-32)Online publication date: 2-Jan-2019
https://dl.acm.org/doi/10.1145/3290350
Ibeling DIcard T(2018)On the conditional logic of simulation modelsProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304889.3304918(1868-1874)Online publication date: 13-Jul-2018
https://dl.acm.org/doi/10.5555/3304889.3304918
Kordjamshidi PRoth DKersting K(2018)Systems AIProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304652.3304785(5464-5471)Online publication date: 13-Jul-2018
https://dl.acm.org/doi/10.5555/3304652.3304785
Borgwardt SCeylan İLukasiewicz T(2018)Recent advances in querying probabilistic knowledge basesProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304652.3304779(5420-5426)Online publication date: 13-Jul-2018
https://dl.acm.org/doi/10.5555/3304652.3304779
Raghothaman MKulkarni SHeo KNaik M(2018)User-guided program reasoning using Bayesian inferenceACM SIGPLAN Notices10.1145/3296979.319241753:4(722-735)Online publication date: 11-Jun-2018
https://dl.acm.org/doi/10.1145/3296979.3192417
Show More Cited By

View Options

View options

Media

Figures

Other

Tables

View Table of Contents