DTProbLog: A Decision-Theoretic Probabilistic Prolog

Guy Van den Broeck, Ingo Thon, Martijn van Otterlo, Luc De Raedt

AAAI 2010 pp. 1217-1222

doi:10.1609/AAAI.V24I1.7755 /aaai/2010/denbroeck2010aaai-dtproblog/

Abstract

We introduce DTProbLog, a decision-theoretic extension of Prolog and its probabilistic variant ProbLog. DTProbLog is a simple but expressive probabilistic programming language that allows the modeling of a wide variety of domains, such as viral marketing. In DTProbLog, the utility of a strategy (a particular choice of actions) is defined as the expected reward for its execution in the presence of probabilistic effects. The key contribution of this paper is the introduction of exact, as well as approximate, solvers to compute the optimal strategy for a DTProbLog program and the decision problem it represents, by making use of binary and algebraic decision diagrams. We also report on experimental results that show the effectiveness and the practical usefulness of the approach.

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Cite

Text

Van den Broeck et al. "DTProbLog: A Decision-Theoretic Probabilistic Prolog." AAAI Conference on Artificial Intelligence, 2010. doi:10.1609/AAAI.V24I1.7755

Markdown

[Van den Broeck et al. "DTProbLog: A Decision-Theoretic Probabilistic Prolog." AAAI Conference on Artificial Intelligence, 2010.](https://mlanthology.org/aaai/2010/denbroeck2010aaai-dtproblog/) doi:10.1609/AAAI.V24I1.7755

BibTeX

@inproceedings{denbroeck2010aaai-dtproblog,
  title     = {{DTProbLog: A Decision-Theoretic Probabilistic Prolog}},
  author    = {Van den Broeck, Guy and Thon, Ingo and van Otterlo, Martijn and De Raedt, Luc},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2010},
  pages     = {1217-1222},
  doi       = {10.1609/AAAI.V24I1.7755},
  url       = {https://mlanthology.org/aaai/2010/denbroeck2010aaai-dtproblog/}
}