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Real-time Strategy Game Micro for Tactical Training Simulations by Sushil J. Louis; Tianyi Jiang; Siming Liu is a scholarly article available to read on EtoBox.

What is Real-time Strategy Game Micro for Tactical Training Simulations about?

Complex, realistic scenarios in training simulations can benefit from good control of large numbers of simulation entities. However, training simulations typically focus on simulation physics and graphics over the intelligence required to control large numbers of entities. Real-Time Strategy games, on the other hand, have evolved to make tradeoffs between the AI needed and human interaction required to control hundreds of entities in complex tactical skirmishes. Borrowing from work in real-time strategy games, this paper attacks the problem of controlling groups of heterogenous entities in training simulations by using a genetic algorithm to evolve control algorithm parameters that maximize damage done and minimize damage received during skirmishes in a real-time strategy game-like simulation. Results show the emergence of complex, coordinated behavior among groups of simulated entities. Evolved behavior quality seems to be relatively independent of the underlying physics model but depends on the initial dispositions of entities in the simulation. We can get over this dependence and evolve more robust high performance behaviors by evaluating fitness in several different scenarios w

Author

Sushil J. Louis; Tianyi Jiang; Siming Liu

Publisher

ACM

Published

2018

Language

EN