id: 06061699
dt: a
an: 06061699
au: Eisner, Jason; Filardo, Nathaniel W.
ti: Dyna: extending datalog for modern AI.
so: de Moor, Oege (ed.) et al., Datalog reloaded. First international workshop,
    Datalog 2010, Oxford, UK, March 16‒19, 2010. Revised selected papers.
    Berlin: Springer (ISBN 978-3-642-24205-2/pbk). Lecture Notes in
    Computer Science 6702, 181-220 (2011).
py: 2011
pu: Berlin: Springer
la: EN
cc: 
ut: 
ci: 
li: doi:10.1007/978-3-642-24206-9_11
ab: Summary: Modern statistical AI systems are quite large and complex; this
    interferes with research, development, and education. We point out that
    most of the computation involves database-like queries and updates on
    complex views of the data. Specifically, recursive queries look up and
    aggregate relevant or potentially relevant values. If the results of
    these queries are memoized for reuse, the memos may need to be
    $updated$ through change propagation. We propose a declarative
    language, which generalizes datalog, to support this work in a generic
    way. Through examples, we show that a broad spectrum of AI algorithms
    can be concisely captured by writing down systems of equations in our
    notation. Many strategies could be used to actually solve those
    systems. Our examples motivate certain extensions to datalog, which are
    connected to functional and object-oriented programming paradigms.
rv: