TY - GEN
T1 - Probabilistic belief revision via imaging
AU - Chhogyal, Kinzang
AU - Nayak, Abhaya
AU - Schwitter, Rolf
AU - Sattar, Abdul
PY - 2014
Y1 - 2014
N2 - While Bayesian conditioning fits in nicely with probabilistic belief expansion, its use is problematic in the context of non-trivial belief revision. Lewis’ use of imaging based on closeness between possible worlds offers a way to overcome this limitation in the context of belief update (in a dynamic environment). In this paper, we explore the use of imaging as a means to construct probabilistic belief revision. Specifically, we present explicit constructions of three candidates strategies, dubbed Naive, Gullible and Cunning, that are based on imaging, and investigate their properties.
AB - While Bayesian conditioning fits in nicely with probabilistic belief expansion, its use is problematic in the context of non-trivial belief revision. Lewis’ use of imaging based on closeness between possible worlds offers a way to overcome this limitation in the context of belief update (in a dynamic environment). In this paper, we explore the use of imaging as a means to construct probabilistic belief revision. Specifically, we present explicit constructions of three candidates strategies, dubbed Naive, Gullible and Cunning, that are based on imaging, and investigate their properties.
UR - http://www.scopus.com/inward/record.url?scp=84911876193&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-13560-1_55
DO - 10.1007/978-3-319-13560-1_55
M3 - Conference proceeding contribution
AN - SCOPUS:84911876193
SN - 9783319135595
VL - 8862
T3 - Lecture Notes in Artificial Intelligence
SP - 694
EP - 707
BT - PRICAI 2014: Trends in Artificial Intelligence
A2 - Pham, Duc-Nghia
A2 - Park, Seong-Bae
PB - Springer, Springer Nature
CY - Cham
T2 - 13th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2014
Y2 - 1 December 2014 through 5 December 2014
ER -