Knowledge compilation
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.
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
01Classes 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.
02Knowledge 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
Sources and credits
This article is adapted from the Wikipedia article “Knowledge compilation”, written by its contributors and licensed under CC BY-SA 4.0. Fathomly has changed the layout, removed citation markers, navigation and maintenance notices, and adjusted punctuation. This adapted version is shared under the same license. For references, see the original article.
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