Compressing probabilistic Prolog programs
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Problog represents a minimal extension of Prolog that supports such stochastic execution.
“Problog (De Raedt et al., 2007), a prominent member of this family, represents a minimal extension of Prolog that supports such stochastic execution.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as De Raedt et al., 2007
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References 23 · Cited by 40
Research works printed but not linked: 15
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- De Raedt, L., & Kersting, K. (2003). Probabilistic logic learning. SIGKDD Explorations, 5:1, 31-48.
- De Raedt, L., Kimmig, A., & Toivonen, H. (2007). ProbLog: A Probabilistic Prolog and its Application in Link Discovery. In M. Veloso (ed.), Proceedings of the 20th International Joint Conference on Artificial Intelligence (pp. 2468- 2473). AAAI Press.
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- Getoor, L., & Taskar, B. (2007) editors, Statistical Relational Learning. MIT Press.
- Koppel, M., Feldman, R., & Segre, A.M. (1994). Bias-Driven Revision of Log- ical Domain Theories. Journal of Artificial Intelligence Research, 1, 159-208.
- Minato, S., Satoh, K., & Sato, T. (2007) Compiling Bayesian Networks by Symbolic Probability Calculation Based on Zero-suppressed BDDs. In M. Veloso (ed.), Proceedings of the 20th International Joint Conference on Artificial Intel- ligence (pp. 2550-2555). AAAI Press.
- Muggleton S. H (1996). Stochastic logic programs. In L. De Raedt (ed.), Advances in Inductive Logic Programming. IOS Press.
- Sato, T. & Kameya, Y. (2001). Parameter learning of logic programs for symbolic-statistical modeling. Journal of AI Research, 15, 391-454.
- Sevon, P., Eronen, L., Hintsanen, P., Kulovesi, K., & Toivonen, H. (2006). Link discovery in graphs derived from biological databases. In U. Leser, F. Naumann, and B. Eckman (Eds.), Data Integration in the Life Sciences 2006, volume 4075 of LNBI. Springer.
- Valiant, L.G. (1979). The complexity of enumeration and reliability problems. SIAM Journal of Computing, 8, 410-411.
- Wrobel, S. (1996). First Order Theory Refinement. In L. De Raedt (ed.), Advances in Inductive Logic Programming. IOS Press.
- Zelle, J.M & Mooney, R.J. (1994). Inducing Deterministic Prolog Parsers From Treebanks: A Machine Learning Approach. In Proceedings of the 12th National Conference on Artificial Intelligence (AAAI-94) (pp. 748-753).
- Authors
- De Raedt, L., Kersting, K., Kimmig, A., Revoredo, K., & Toivonen, H.
- Affiliations
- University of Freiburg (DE)
- KU Leuven (BE)
- University of Helsinki (FI)
- Corresponding author
- Kimmig, A.
- Venue
- Machine Learning
- Published
- 2007-11-08
- Volume
- 70
- Issue
- 2-3
- Pages
- 151-168
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2148550253
- Cited by
- 40
- References
- 23
- Citations by year
- 2023: 2 · 2022: 2 · 2021: 5 · 2020: 1 · 2019: 1 · 2018: 1 · 2017: 1 · 2016: 1 · 2015: 1 · 2014: 2 · 2013: 2 · 2012: 4
- FWCI
- 5.22
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Machine Learning (publishedVersion)
- Lirias (KU Leuven) (submittedVersion, other-oa)
- Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion)
- Työväentutkimus Vuosikirja (submittedVersion, other-oa)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Funders
- Alexander von Humboldt-Stiftung
- Tekes
- Conselho Nacional de Desenvolvimento Científico e Tecnológico
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Keywords
- ProbLog, probabilistic logic programming, program compression, clause selection, likelihood maximization, learning from examples, success probability
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