Robust Action Strategies to Induce Desired Effects

Abstract

This paper provides a new methodology for obtaining a near-optimal strategy (i.e., specification of courses of action over time) for achieving the desired effects in a mission environment that also is robust to environmental perturbations (i.e., unexpected events and/or parameter uncertainties). A dynamic Bayesian network (DBN)-based stochastic mission model is employed to represent the dynamic and uncertain nature of the environment. Genetic algorithms are applied to search for a near-optimal strategy with DBN serving as a fitness evaluator. The probability of achieving the desired effects (namely, the probability of success) at a specified terminal time is a random variable due to uncertainties in the environment. Consequently, the authors focus on signal-to-noise ratio (SNR), a measure of mean and variance of the probability of success, to gauge the goodness of a strategy. The resulting strategy will not only have a relatively high probability of inducing the desired effects, but also be robust to environmental uncertainties.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2002
Accession Number
ADA440391

Entities

People

  • Haiying Tu
  • Krishna R. Pattipati
  • Yuri N. Levchuk

Organizations

  • University of Connecticut

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Bayesian Networks
  • Commerce
  • Computational Science
  • Electronic Mail
  • Environment
  • Genetic Algorithms
  • Models
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Random Variables
  • Reasoning
  • Simulations
  • Standards
  • Statistical Analysis

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computational Modeling and Simulation
  • Systems Analysis and Design

Technology Areas

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