TY - GEN
T1 - Handling uncertainty in answer set programming
AU - Wang, Yi
AU - Lee, Joohyung
N1 - Publisher Copyright:
© Copyright 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2015/6/1
Y1 - 2015/6/1
N2 - We present a probabilistic extension of logic programs under the stable model semantics, inspired by the concept of Markov Logic Networks. The proposed language takes advantage of both formalisms in a single framework, allowing us to represent commonsense reasoning problems that require both logical and probabilistic reasoning in an intuitive and elaboration tolerant way.
AB - We present a probabilistic extension of logic programs under the stable model semantics, inspired by the concept of Markov Logic Networks. The proposed language takes advantage of both formalisms in a single framework, allowing us to represent commonsense reasoning problems that require both logical and probabilistic reasoning in an intuitive and elaboration tolerant way.
UR - http://www.scopus.com/inward/record.url?scp=84961206136&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84961206136&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84961206136
T3 - Proceedings of the National Conference on Artificial Intelligence
SP - 4218
EP - 4219
BT - Proceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015
PB - AI Access Foundation
T2 - 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015
Y2 - 25 January 2015 through 30 January 2015
ER -