Sequential Prediction for Information Fusion and Control

Abstract

The goal of this work was to develop sequential prediction methods for online information fusion and control, with methods designed to handle unknown, environmental dynamics, potentially stemming from an adversary who reacts to sensing actions, active sensing paradigms, and external feedback mechanisms. Online prediction and targeted collection of information is an emerging paradigm at the intersection of optimization, machine learning and control theory, which is concerned with real-time sequential planning of actions or decisions in the presence of model uncertainty, nonstationarity, and possibly adversarial disturbances. Several methods and underlying supporting theory which meet these objectives are described in detail in the following final report.

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Document Details

Document Type
Technical Report
Publication Date
Oct 14, 2013
Accession Number
ADA594438

Entities

People

  • Maxim Raginsky
  • Rebecca Willett

Organizations

  • Duke University

Tags

Communities of Interest

  • Human Systems
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Algorithms
  • Computer Programming
  • Convex Programming
  • Distance Learning
  • Errors
  • Linear Programming
  • Machine Learning
  • Markov Chains
  • Markov Processes
  • Optimization
  • Probability
  • Probability Distributions
  • Random Variables
  • Random Walk
  • Steady State
  • Trees (Data Structures)

Readers

  • Computational Modeling and Simulation
  • Distributed Systems and Data Platform Development
  • Statistical inference.

Technology Areas

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