Information Driven, Adaptive Distributed Planning

Abstract

This report details our approach to combining dynamic, distributed constraint reasoning with machine learning techniques and adaptive response strategies. By combining these technologies, we built a system that can 1) develop robust, adaptable mission plans 2) exploit knowledge learned through prior interactions with our adversary, and 3) autonomously and dynamically alter its behavior during mission execution to improve the likelihood of a successful outcome. This system has been thoroughly tested in the ATE2 and ATE3 simulators that were provided by AFRL/RI against four increasingly difficult milestones.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2019
Accession Number
AD1074604

Entities

People

  • Roger Mailler
  • Rose Gamble

Organizations

  • University of Tulsa

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Aircrafts
  • Algorithms
  • Artificial Intelligence
  • Contracts
  • Data Science
  • Digital Data
  • Distance Learning
  • Equations
  • Gaussian Distributions
  • Government Procurement
  • Governments
  • Grids
  • Information Science
  • Information Systems
  • Learning
  • Machine Learning
  • Motion Planning
  • Multiagent Systems
  • Probability
  • Reasoning
  • Reinforcement Learning
  • Simulations
  • Simulators
  • Step Functions
  • Two Dimensional
  • Unmanned Aerial Vehicles

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Software Engineering

Technology Areas

  • AI & ML