id: 05574370
dt: a
an: 05574370
au: Baltag, Alexandru; Smets, Sonja
ti: Learning by questions and answers: from belief-revision cycles to doxastic
    fixed points.
so: Ono, Hiroakira (ed.) et al., Logic, language, information and computation.
    16th international workshop, WoLLIC 2009, Tokyo, Japan, June 21‒24,
    2009. Proceedings. Berlin: Springer (ISBN 978-3-642-02260-9/pbk).
    Lecture Notes in Computer Science 5514. Lecture Notes in Artificial
    Intelligence, 124-139 (2009).
py: 2009
pu: Berlin: Springer
la: EN
cc: 
ut: 
ci: 
li: doi:10.1007/978-3-642-02261-6_11
ab: Summary: We investigate the long-term behavior of iterated belief revision
    with higher-level doxastic information. While the classical literature
    on iterated belief revision deals only with propositional information,
    we are interested in learning (by an introspective agent, of some
    partial information about the) answers to various questions $Q _{1}, Q
    _{2}, \dots , Q _{n }, \dots $ that may refer to the agent’s own
    beliefs (or even to her belief-revision plans). Here, “learning”
    can be taken either in the “hard” sense (of becoming absolutely
    certain of the answer) or in the “soft” sense (accepting some
    answers as more plausible than others). If the questions are binary
    (“is $φ$ true or not?”), the agent “learns” a sequence of true
    doxastic sentences $φ_{1}, \dots , φ_{n }, \dots $. “Investigating
    the long-term behavior” of this process means that we are interested
    in whether or not the agent’s beliefs, her “knowledge” and her
    conditional beliefs stabilize eventually or keep changing forever.
rv: