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Proceedings Paper

Building intelligence in third-generation training and battle simulations
Author(s): Dennis Jacobi; Don Anderson; Vance von Borries; Adel Elmaghraby; Mehmed Kantardzic; Rammohan Ragade
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Paper Abstract

Current war games and simulations are primarily attrition based, and are centered on the concept of force on force. They constitute what can be defined as “second generation” war games. So-called “first generation” war games were focused on strategy with the primary concept of mind on mind. We envision “third generation” war games and battle simulations as concentrating on effects with the primary concept being system on system. Thus the third generation systems will incorporate each successive generation and take into account strategy, attrition and effects. This paper will describe the principal advantages and features that need to be implemented to create a true “third generation” battle simulation and the architectural issues faced when designing and building such a system. Areas of primary concern are doctrine, command and control, allied and coalition warfare, and cascading effects. Effectively addressing the interactive effects of these issues is of critical importance. In order to provide an adaptable and modular system that will accept future modifications and additions with relative ease, we are researching the use of a distributed Multi-Agent System (MAS) that incorporates various artificial intelligence methods. The agent architecture can mirror the military command structure from both vertical and horizontal perspectives while providing the ability to make modifications to doctrine, command structures, inter-command communications, as well as model the results of various effects upon one another, and upon the components of the simulation. This is commonly referred to as “cascading effects,” in which A affects B, B affects C and so on. Agents can be used to simulate units or parts of units that interact to form the whole. Even individuals can eventually be simulated to take into account the affect to key individuals such as commanders, heroes, and aces. Each agent will have a learning component built in to provide “individual intelligence” based on experience.

Paper Details

Date Published: 4 September 2003
PDF: 10 pages
Proc. SPIE 5091, Enabling Technologies for Simulation Science VII, (4 September 2003); doi: 10.1117/12.500901
Show Author Affiliations
Dennis Jacobi, Intellas Modeling and Simulation, LLC (United States)
Don Anderson, Intellas Modeling and Simulation, LLC (United States)
Vance von Borries, Intellas Modeling and Simulation, LLC (United States)
Adel Elmaghraby, Univ. of Louisville (United States)
Mehmed Kantardzic, Univ. of Louisville (United States)
Rammohan Ragade, Univ. of Louisville (United States)


Published in SPIE Proceedings Vol. 5091:
Enabling Technologies for Simulation Science VII
Alex F. Sisti; Dawn A. Trevisani, Editor(s)

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