A probabilistic object-oriented data model

Yoram Kornatzky, Solomon Eyal Shimony

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


Intelligent systems such as autonomous robots interacting with the real world encounter uncertainty in their knowledge about the world, due to incomplete information, data acquisition and measurement errors, and uncertainty about cause and effect. To operate in such an environment, uncertain information needs to be represented explicitly in their world model (database). We outline an object-oriented data model that describes uncertainty through the use of (Bayesian) probabilities. The model represents uncertainty with respect to values of attributes, and with respect to the class hierarchy.

Original languageEnglish
Pages (from-to)143-166
Number of pages24
JournalData and Knowledge Engineering
Issue number2
StatePublished - 1 Jan 1994


  • Object-oriented data model
  • inheritance
  • probability
  • robotics
  • uncertain information

ASJC Scopus subject areas

  • Information Systems and Management


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