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Probabilistic logic programming with beta-distributed random variables

Cerutti, Federico, Kaplan, Lance, Kimmig, Angelika and Sensoy, Murat 2018. Probabilistic logic programming with beta-distributed random variables. Presented at: AAAI-19: 33rd AAAI Conference on Artificial Intelligence, Honolulu, HI, USA, 27 January - 1 February 2019.

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Abstract

We enable aProbLog—a probabilistic logical programming approach—to reason in presence of uncertain probabilities represented as Beta-distributed random variables. We achieve the same performance of state-of-the-art algorithms for highly specified and engineered domains, while simultaneously we maintain the flexibility offered by aProbLog in handling complex relational domains. Our motivation is that faithfully capturing the distribution of probabilities is necessary to compute an expected utility for effective decision making under uncertainty: unfortunately, these probability distributions can be highly uncertain due to sparse data. To understand and accurately manipulate such probability distributions we need a well-defined theoretical framework that is provided by the Beta distribution, which specifies a distribution of probabilities representing all the possible values of a probability when the exact value is unknown.

Item Type: Conference or Workshop Item (Paper)
Date Type: Completion
Status: Unpublished
Schools: Computer Science & Informatics
Crime and Security Research Institute (CSURI)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
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Date of First Compliant Deposit: 20 December 2018
Last Modified: 20 Dec 2018 16:37
URI: http://orca.cf.ac.uk/id/eprint/116818

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