10.1155/2011/834026">
 

Document Type

Article

Publication Date

3-2-2011

Abstract

The underlying goal of a competing agent in a discrete real-time strategy (RTS) game is to defeat an adversary. Strategic agents or participants must define an a priori plan to maneuver their resources in order to destroy the adversary and the adversary's resources as well as secure physical regions of the environment. This a priori plan can be generated by leveraging collected historical knowledge about the environment. This knowledge is then employed in the generation of a classification model for real-time decision-making in the RTS domain. The best way to generate a classification model for a complex problem domain depends on the characteristics of the solution space. An experimental method to determine solution space (search landscape) characteristics is through analysis of historical algorithm performance for solving the specific problem. We select a deterministic search technique and a stochastic search method for a priori classification model generation. These approaches are designed, implemented, and tested for a specific complex RTS game, Bos Wars. Their performance allows us to draw various conclusions about applying a competing agent in complex search landscapes associated with RTS games.

Comments

Copyright © 2011 Kurt Weissgerber et al.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. CC BY 3.0

The published version is furnished on AFIT Scholar in accordance with sharing rules found at Open Policy Finder for the source journal.

Source Publication

International Journal of Computer Games Technology (ISSN 1687-7047 | e-ISSN 1687-7055)

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