{{Notability|date=August 2022}} '''Valuation-based system''' (VBS) is a framework for [[knowledge representation]] and inference. Real-world problems are modeled in this framework by a network of interrelated entities, called variables. The relationships between variables (possibly uncertain or imprecise) are represented by the functions called valuations. The two basic operations for performing inference in a VBS are combination and marginalization. Combination corresponds to the aggregation of knowledge, while marginalization refers to the focusing (coarsening) of it. VBSs were introduced by Prakash P. [[Shenoy]] in 1989 as general frameworks for managing uncertainty in [[expert system]]s.
==Applications== VBS are used for knowledge representation in expert systems and data fusion.
==Bibliography== * Shenoy, Prakash P. A valuation-based language for expert systems. ''Int. Journal of Approximate Reasoning'', vol. 3, no. 2, pages 383–411, 1989. * Shenoy, Prakash P. Valuation based systems: A framework for managing uncertainty in expert systems. In L. A. Zadeh and J. Kacprzyk, editors, ''Fuzzy Logic and the Management of Uncertainty'', chapter 4, pages 83–104. Wiley, New York, 1992. * Shenoy, Prakash P. and Shafer, G. Axioms for probability and belief-function propagation. In J. Pearl G. Shafer, editor, ''Readings in uncertain reasoning'', pages 575–610. San Mateo, CA: Morgan Kaufmann, 1990.
==External links== *[http://www.idsia.ch/~alessio/TAES-VBS.pdf Application of VBS to a threat assessment problem]
[[Category:Logic]]