Probabilistic belief revision via imaging

No Thumbnail Available
File version
Author(s)
Chhogyal, Kinzang
Nayak, Abhaya
Schwitter, Rolf
Sattar, Abdul
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)

Pham, DN

Park, SB

Date
2014
Size
File type(s)
Location

Gold Coast, AUSTRALIA

License
Abstract

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.

Journal Title
Conference Title

PRICAI 2014: TRENDS IN ARTIFICIAL INTELLIGENCE

Book Title
Edition
Volume

8862

Issue
Thesis Type
Degree Program
School
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement
Item Access Status
Note
Access the data
Related item(s)
Subject

Artificial intelligence not elsewhere classified

Persistent link to this record
Citation