Uncertainty Management for Dynamic Decision Making

Abstract

Major Goals: There are two goals in this project: 1. Measure uncertainty in beliefs considering multiple root causes of uncertainty: We extend an existing belief model, called Subjective Logic (SL), to measure uncertainty in beliefs based on the root causes of uncertainty, including lack of information or knowledge, vagueness, and ambiguity. Although SL is developed to deal with the dimension of uncertainty explicitly unlike the existing belief models, SL considers uncertainty derived from lack of information and vagueness but does not deal with ambiguity derived from conflicting evidence. 2. Reduce uncertainty in data with high scalability: We develop scalable uncertainty reduction algorithms by identifying the minimum set of data features to maximize decision effectiveness. A decision-maker often loses high utility by delaying a decision due to numerous alternative decisions with an equal utility or a high volume of uncertain evidence. Our goal is to significantly reduce uncertainty using a minimum set of features while maximizing classification accuracy.

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Document Details

Document Type
Technical Report
Publication Date
Nov 22, 2019
Accession Number
AD1112282

Entities

People

  • Feng Chen

Organizations

  • State University of New York at Albany

Tags

Communities of Interest

  • Engineered Resilient Systems
  • Human Systems

DTIC Thesaurus Topics

  • Abstracts
  • Accuracy
  • Ambiguity
  • Artificial Intelligence Software
  • Bayesian Networks
  • Big Data
  • Classification
  • Convolutional Neural Networks
  • Data Mining
  • Deep Learning
  • Intelligent Systems
  • Learning
  • Machine Learning
  • Models
  • Neural Networks
  • Probabilistic Models
  • Students

Readers

  • Regression Analysis.
  • Systems Analysis and Design
  • Theoretical Analysis.