Case-Based Parameter Selection for Plans: Coordinating Autonomous Vehicle Teams

Abstract

Executing complex plans for coordinating the behaviors of multiple heterogeneous agents often requires setting several parameters. For example, we are developing a decision aid for deploying a set of autonomous vehicles to perform situation assessment in a disaster relief operation. Our system, the Situated Decision Process (SDP), uses parameterized plans to coordinate these vehicles. However, no model exists for setting the values of these parameters. We describe a case-based reasoning solution for this problem and report on its utility in simulated scenarios, given a case library that represents only a small percentage of the problem space. We found that our agents, when executing plans generated using our case-based algorithm on problems with high uncertainty performed signi cantly better than when executing plans using baseline approaches.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2014
Accession Number
ADA618109

Entities

People

  • Bryan Auslander
  • David W. Aha
  • Tom Apker

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Aircrafts
  • Algorithms
  • Area Coverage
  • Artificial Intelligence
  • Autonomous Vehicles
  • Disasters
  • Energy Consumption
  • Genetic Algorithms
  • Humanitarian Assistance
  • Information Science
  • Military Applications
  • Reasoning
  • Simulations
  • Uncertainty
  • Unmanned Aerial Systems
  • Unmanned Vehicles
  • Vehicles

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Distributed Systems and Data Platform Development
  • Operations Research

Technology Areas

  • Autonomy
  • Autonomy - Autonomous System Control
  • Space
  • Space - Spacecraft Maneuvers