Case-Based Behavior Recognition to Facilitate Planning in Unmanned Air Vehicles

Abstract

An unmanned air vehicle (UAV) can operate as a capable team member in mixed human-robot teams if the agent that controls it can intelligently plan. However, planning effectively in an air combat scenario requires understanding the behaviors of hostile agents in that scenario, which is challenging in partially observable environments such as the one we study. We present a Case-Based Behavior Recognition (CBBR) algorithm that annotates an agent's behaviors using a discrete feature set derived from a continuous spatio-temporal world state. These behaviors can then be given as input to an air combat simulation, along with the UAV's plan, to predict hostile actions and determine the effectiveness of the given plan. We describe an initial implementation of a CBBR prototype in the context of a goal reasoning agent designed for UAV control.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2014
Accession Number
ADA621410

Entities

People

  • David W. Aha
  • Hayley Borck
  • Justin Karneeb
  • Ron Alford

Organizations

  • Knexus Research (United States)

Tags

Communities of Interest

  • Air Platforms
  • Autonomy

DTIC Thesaurus Topics

  • Aircrafts
  • Algorithms
  • Artificial Intelligence
  • Computational Science
  • Genetic Algorithms
  • Military Research
  • Reasoning
  • Recognition
  • Simulations
  • Simulators
  • Unmanned
  • Unmanned Aerial Vehicles
  • Unmanned Vehicles
  • Vehicles
  • Warfare

Fields of Study

  • Computer science

Readers

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

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • Autonomy
  • Autonomy - UAVs