Probabilistic logic programming with beta-distributed random variables

Name: Probabilistic logic programming with beta-distributed random variables
Author: Cerutti, F., Kaplan, L., Kimmig, A., Şensoy, Murat

İsim	Probabilistic logic programming with beta-distributed random variables
Yazar	Cerutti, F., Kaplan, L., Kimmig, A., Şensoy, Murat
Basım Tarihi:	2019-07-17
Basım Yeri	- Association for the Advancement of Artificial Intelligence
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-1-57735-809-1
Kayıt Numarası	9003af5e-27e4-45f5-9eb7-14d02d1d01a1
Lokasyon	Computer Science
Tarih	2019-07-17
Notlar	United States Department of Defense US Army Research Laboratory (ARL) ; U.K. Ministry of Defence, The Association for the Advancement of Artificial Intelligence
Örnek Metin	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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Probabilistic logic programming with beta-distributed random variables

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

Basım Tarihi 2019-07-17

Basım Yeri - Association for the Advancement of Artificial Intelligence

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-1-57735-809-1

Kayıt Numarası 9003af5e-27e4-45f5-9eb7-14d02d1d01a1

Lokasyon Computer Science

Tarih 2019-07-17

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

Örnek Metin 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