Deriving Optimal Solutions from Incomplete Knowledge Bases

Abstract

Many real world domains can not be represented using Bayesian Networks due to the need for complete probability tables and acyclic knowledge. However, Bayesian Knowledge Bases (BKBs) are a viable method for representing these incomplete domains, but very little research has been performed on inferencing with them. This paper presents three inference engines for extracting optimal solutions from three distinct BKB subclasses: singly- connected, multiply-connected with mutually exclusive cycles, and cyclic. The singly-connected inference engine has a worst case polynomial run time. Performance improvement techniques for increasing inference engine speed are discussed, in addition to a new tool for measuring incompleteness and aiding in BKB Validation & Verification.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1995
Accession Number
ADA303826

Entities

People

  • Shawn A. Northrop

Organizations

  • Air Force Institute of Technology

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Bayesian Networks
  • Computer Programs
  • Computer Science
  • Computers
  • Expert Systems
  • Inference Engines
  • Integer Programming
  • Linear Programming
  • Operations Research
  • Polynomials
  • Probability
  • Probability Distributions
  • Reasoning
  • Simplex Method
  • Validation

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Computational Modeling and Simulation

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks