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Probabilistic logic programming with beta-distributed random variables

Title	Probabilistic logic programming with beta-distributed random variables
Author	Cerutti, F., Kaplan, L., Kimmig, A., Şensoy, Murat
Publication Date:	2019-07-17
Publication Place	- Association for the Advancement of Artificial Intelligence
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	978-1-57735-809-1
Record ID	9003af5e-27e4-45f5-9eb7-14d02d1d01a1
Library Location	Computer Science
Date	2019-07-17
Notes	United States Department of Defense US Army Research Laboratory (ARL) ; U.K. Ministry of Defence, The Association for the Advancement of Artificial Intelligence
Sample Text	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.
Cilt	33

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Özyeğin University

Probabilistic logic programming with beta-distributed random variables

Author Cerutti, F., Kaplan, L., Kimmig, A., Şensoy, Murat

Publication Date 2019-07-17

Publication Place - Association for the Advancement of Artificial Intelligence

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 978-1-57735-809-1

Record ID 9003af5e-27e4-45f5-9eb7-14d02d1d01a1

Library Location Computer Science

Date 2019-07-17

Notes United States Department of Defense US Army Research Laboratory (ARL) ; U.K. Ministry of Defence, The Association for the Advancement of Artificial Intelligence

Sample Text 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.

Cilt 33