Open-universe probability models show merit in unifying efforts: First-order logic and probability theory have addressed complementary aspects of knowledge representation and reasoning: the ability to describe complex domains concisely in terms of objects and relations and the ability to handle uncertain information. Their unification holds enormous promise for AI. New languages for defining open-universe probability models appear to provide the desired unification in a natural way. As a bonus, they support probabilistic reasoning about the existence and identity of objects, which is important for any system trying to understand the world through perceptual or textual inputs.
%0 Journal Article
%1 Russell15cacm
%A Russell, Stuart
%D 2015
%J Communications of the ACM
%K 01801 acm paper numerical ai knowledge processing logic theory
%N 7
%P 88--97
%R 10.1145/2699411
%T Recent Developments in Unifying Logic and Probability
%V 58
%X Open-universe probability models show merit in unifying efforts: First-order logic and probability theory have addressed complementary aspects of knowledge representation and reasoning: the ability to describe complex domains concisely in terms of objects and relations and the ability to handle uncertain information. Their unification holds enormous promise for AI. New languages for defining open-universe probability models appear to provide the desired unification in a natural way. As a bonus, they support probabilistic reasoning about the existence and identity of objects, which is important for any system trying to understand the world through perceptual or textual inputs.
@article{Russell15cacm,
abstract = {Open-universe probability models show merit in unifying efforts: First-order logic and probability theory have addressed complementary aspects of knowledge representation and reasoning: the ability to describe complex domains concisely in terms of objects and relations and the ability to handle uncertain information. Their unification holds enormous promise for AI. New languages for defining open-universe probability models appear to provide the desired unification in a natural way. As a bonus, they support probabilistic reasoning about the existence and identity of objects, which is important for any system trying to understand the world through perceptual or textual inputs.},
added-at = {2016-11-28T16:08:27.000+0100},
author = {Russell, Stuart},
biburl = {https://www.bibsonomy.org/bibtex/24b78b7ad2c73951106d1252349ef6a7a/flint63},
doi = {10.1145/2699411},
file = {ACM Digital Library:2015/Russell15cacm.pdf:PDF},
groups = {public},
interhash = {320ee86d8a78b4e9bb4f5381c0a14527},
intrahash = {4b78b7ad2c73951106d1252349ef6a7a},
issn = {0001-0782},
journal = {Communications of the ACM},
keywords = {01801 acm paper numerical ai knowledge processing logic theory},
month = {#jul#},
number = 7,
pages = {88--97},
timestamp = {2018-04-16T12:23:08.000+0200},
title = {Recent Developments in Unifying Logic and Probability},
username = {flint63},
volume = 58,
year = 2015
}