Multi-state Directed Acyclic Graphs

  • Michael Wachter
  • Rolf Haenni
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4509)


This paper continues the line of research on the representation and compilation of propositional knowledge bases with propositional directed acyclic graphs (PDAG), negation normal forms (NNF), and binary decision diagrams (BDD). The idea is to permit variables with more than two states and to explicitly represent them in their most natural way. The resulting representation languages are analyzed according to their succinctness, supported queries, and supported transformations. The paper shows that most results from PDAGs, NNFs, and BDDs can be generalized to their corresponding multi-state extension. This implies that the entire knowledge compilation map is extensible from propositional to multi-state variables.


Bayesian Network Boolean Function Auxiliary Variable Boolean Variable Decision Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Michael Wachter
    • 1
  • Rolf Haenni
    • 1
    • 2
  1. 1.University of BernSwitzerland
  2. 2.Bern University of Applied SciencesSwitzerland

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