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 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
[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
 | This artificial intelligence–related article is a stub. You can help Wikipedia by adding missing information. |