Information Theoretic Causal Coordination

Abstract

Automated decision-making over large-scale distributed systems in the presence of uncertainty and incomplete information is a formidable task. The traditional view of information fusion and decision making over sensor networks is heavily biased by the fact that dependencies between information sources are treated only in terms of correlation. Inspired by brain's excellent job at processing information, we make a departure from this traditional view point by realizing that: human judgments about the likelihood of events and dependencies among variables are strongly influenced by the perception of cause-effect relationships. In terms of incorporating causality, we pursued formulation of information-theoretic causality metrics such as directed information. We developed a general framework for inferring causal influences in stochastic networks as well as information fusion in online recommendation systems.

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Document Details

Document Type
Technical Report
Publication Date
Sep 12, 2013
Accession Number
ADA592847

Entities

People

  • Negar Kiyavash

Organizations

  • University of Illinois Urbana–Champaign

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Causal Reasoning
  • Compressed Sensing
  • Detectors
  • Generative Models
  • Illinois
  • Information Theory
  • Models
  • Networks
  • Probability
  • Probability Distributions
  • Reasoning
  • Scheduling (Production)
  • Sensor Networks
  • Signal Processing
  • Stochastic Processes
  • Students

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Systems Analysis and Design
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

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