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Article
Evolving Intelligent Agents for First Responder Training Simulation
Intelligent Systems Through Artificial Neural Networks ANNIE '04
  • Alex J. Berry
  • Daniel R. Tauritz, Missouri University of Science and Technology
  • Michael Gene Hilgers, Missouri University of Science and Technology
Abstract

Many training simulations can benefit from increased levels of reality obtained through the use of intelligent autonomous agents. The optimization of multiple interacting agent programs is an open research problem. Evolutionary Algorithms allow for an efficient direct stochastic search of the (near) infinite space of all possible agent programs. This paper describes a proof-of-concept experiment for evolving autonomous agents in a two dimensional environment. Each of the agent types has its own goals to enhance or reduce the hostility of the environment, which are correlated to a fitness value. The results show that genetic programming can be successfully be employed to optimize the agent programs. With further work, agents should be able to operate in a virtual world to train first responders.

Department(s)
Computer Science
Second Department
Business and Information Technology
Sponsor(s)
United States. Army
Keywords and Phrases
  • Evolutionary Algorithms,
  • Genetic Programming,
  • Training Simulations
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2004 American Society of Mechanical Engineers (ASME), All rights reserved.
Publication Date
1-1-2004
Disciplines
Citation Information
Alex J. Berry, Daniel R. Tauritz and Michael Gene Hilgers. "Evolving Intelligent Agents for First Responder Training Simulation" Intelligent Systems Through Artificial Neural Networks ANNIE '04 (2004)
Available at: http://works.bepress.com/daniel-tauritz/38/