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 polytime. Many ways of compiling a propositional models exist.[1]
Different compiled representations have different properties. The three main properties are:
- The compactness of the representation
- The queries that are supported in polytime
- The transformations of the representations that can be performed in polytime
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.
gollark: I can also bias it the other way.
gollark: I could add that as an actual option, I suppose.
gollark: That was actually a bugged version where it was very slightly biased toward darkness.
gollark: Also, minor apionic issues of some sort.
gollark: It runs with the wondrous performance of javascriptoidal forms.
References
- Adnan Darwiche, Pierre Marquis, "A Knowledge Compilation Map", Journal of Artificial Intelligence Research 17 (2002) 229-264
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