A Multi-Objective Approach to Tactical Maneuvering Within Real Time Strategy Games

Abstract

The real time strategy (RTS) environment is a strong platform for simulating complex tactical problems. The overall research goal is to develop artificial intelligence (AI) RTS planning agents for military critical decision making education.This particular research effort of RTS AI development focuses on constructing a unique approach for tactical unit positioning within an RTS environment. By utilizing multiobjective evolutionary algorithms (MOEAs) for finding an optimal positioning solution, an AI agent can quickly determine an effective unit positioning solution with a fast, rapid response. The resulting agent does not require the usage of training or tree searches to optimize, allowing for consist effective performance across all scenarios against a variety of opposing tactical options.

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Document Details

Document Type
Technical Report
Publication Date
Jun 16, 2016
Accession Number
AD1054214

Entities

People

  • Christopher D. Ball

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Air Force
  • Air Power
  • Algorithms
  • Artificial Intelligence
  • Computers
  • Data Sets
  • Department Of Defense
  • Evolutionary Algorithms
  • Genetic Algorithms
  • Governments
  • Military Operations
  • Military Science
  • Particle Swarm Optimization
  • Students
  • United States
  • United States Government
  • Warfare

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Aerospace Engineering.
  • Systems Analysis and Design

Technology Areas

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