{{Short description|AI methods}} '''Knowledge compilation''' is a family of approaches for addressing the intractability of a number of artificial intelligence problems.
A propositional model is compiled in an off-line phase in order to support some queries in polynomial time. Many ways of compiling a propositional model exist.<ref name="darwiche">Adnan Darwiche, Pierre Marquis, "[https://doi.org/10.1613/jair.989 A Knowledge Compilation Map]", Journal of Artificial Intelligence Research 17 (2002) 229-264</ref>
Different compiled representations have different properties. The three main properties are: * The compactness of the representation * The queries that are supported in polynomial time * The transformations of the representations that can be performed in polynomial time
== Classes of representations ==
Some examples of diagram classes include decision trees, OBDDs, FBDDs, and non-deterministic OBDDs, as well as MDD.
Some examples of formula classes include DNF and CNF.
Examples of circuit classes include NNF, DNNF, d-DNNF, and SDD.
== Knowledge compilers ==
* c2d: supports compilation to d-DNNF * d4: supports compilation to d-DNNF * miniC2D: supports compilation to SDD * KCBox: supports compilation to OBDD, OBDD[AND], and CCDD
== References == <references/>
{{DEFAULTSORT:Knowledge Compilation}} Category:Artificial intelligence
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