Knowledge compilation

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.[1]

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 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

1. Adnan Darwiche, Pierre Marquis, "A Knowledge Compilation Map", Journal of Artificial Intelligence Research 17 (2002) 229-264