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[edit]
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[edit]
- 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[edit]
- ^ Adnan Darwiche, Pierre Marquis, "A Knowledge Compilation Map", Journal of Artificial Intelligence Research 17 (2002) 229-264
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