Computing with Bayesian Multi-Networks

Abstract

Existing probabilistic approaches to automated reasoning impose severe restrictions on its knowledge representation scheme. Mainly, this is to ensure that there exists an effective inferencing algorithm. Unfortunately, this makes the application of these approaches to general domains quite difficult. In this paper, we present a new mode' called Bayesian multi-networks which uses a rule-based organization of knowledge quite natural for human experts modeling various domains. Furthermore, strong probabilistic semantics help quantify the knowledge. Combined with the rich structure of rule-based approaches, a general inference engine for Bayesian multi-networks is developed. Probabilistic reasoning, Constraint satisfaction, Linear programming, Temporal reasoning, Abductive explanation.

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Document Details

Document Type
Technical Report
Publication Date
Nov 16, 1993
Accession Number
ADA273106

Entities

People

  • Eugene Santos

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Artificial Intelligence Computing
  • Bayesian Networks
  • Computational Science
  • Computations
  • Computer Programming
  • Convex Sets
  • Databases
  • Integer Programming
  • Linear Programming
  • Models
  • Probability
  • Random Variables
  • Real Variables
  • Reasoning

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms