Using Structured Knowledge Representation for Context-Sensitive Probabilistic Modeling

Abstract

We propose a context-sensitive probabilistic modeling system (COSMOS) that reasons about a complex, dynamic environment through a series of applications of smaller, knowledge-focused models representing contextually relevant information. COSMOS uses a failure-driven architecture to determine whether a context is supported and consequently whether the current model remains applicable. The individual models are specified through sets of structured, hierarchically organized probabilistic logic statements using transfer functions that are then mapped into a representation supporting stochastic inferencing. We demonstrate COSMOS using data from a mechanical pump system.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2008
Accession Number
ADA491876

Entities

People

  • George F. Luger
  • Nikita A. Sakhanenko

Organizations

  • University of New Mexico

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Bayesian Networks
  • Computational Science
  • Damage Detection
  • Data Sets
  • Detectors
  • Environment
  • Hidden Markov Models
  • Language
  • Monitoring
  • Neural Networks
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Random Variables
  • Reasoning
  • Transfer Functions

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Systems Analysis and Design