Combining Facts and Expert Opinion in Analytical Models via Logical and Probabilistic Reasoning

Abstract

This report describes work on an integrated system that can assist analysts in exploring hypotheses using Bayesian analysis of evidence from a variety of sources. The hypothesis exploration is aided by an ontology that represents domain knowledge, events, and causality for Bayesian reasoning, as well as models of information sources for evidential reasoning. We are validating the approach via a tool, Magellan, that uses both Bayesian models and logical models for an analyst's prior and tacit knowledge about how evidence can be used to evaluate hypotheses. We also describe how we combine logic information, in the form of proofs provided by the natural deduction system SILK(Semantic Inferencing on Large Knowledge) and probabilistic information, represented by Bayesian networks, in the BRUSE(Bayesian Reasoning Using Soft Evidence) system.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2010
Accession Number
ADA519886

Entities

People

  • John Byrnes
  • Marco Valtorta
  • Michael Huhns

Organizations

  • University of South Carolina

Tags

Communities of Interest

  • Energy and Power Technologies
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Bayesian Networks
  • Delphi Method
  • Government Procurement
  • Governments
  • Hypotheses
  • Intelligence Analysis
  • Intelligence Analysts
  • Models
  • Numerical Analysis
  • Ontologies
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Reasoning

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval