|Résumé||Knowledge compilation aims to transform knowledge on a system, often
represented in a natural but hard to query way, into a - hopefully small - data
structure that is more practical to reason with. The idea is that after a
potentially expensive offline compilation phase, one can efficiently extract new
knowledge by directly querying the data structure.
Originating in symbolic AI where it was mainly used to find better
representations of CNF-formulas or to efficiently solve configuration problems,
this idea has spread in many adjacent fields such as database theory where it
has been used to efficiently compute aggregates in databases.
In this talk, we will give a broad overview of knowledge compilation and its
applications. We will presente some interesting data structure used in knowledge
compilation and how they are used both theoretically and in practice. |