Stochastic Plan Optimization in Real-Time Strategy Games

We present a domain independent off-line adaptation technique called Stochastic Plan Optimization for finding and improving plans in real-time strategy games. Our method is based on ideas from genetic algorithms, but we utilize a different representation for our plans and an alternate initialization procedure for our search process. The key to our technique is the use of expert plans to initialize our search in the most relevant parts of plan space. Our experiments validate this approach using our existing case based reasoning system Darmok in the real-time strategy game Wargus, a clone of Warcraft II.

Read the paper:

Stochastic Plan Optimization in Real-Time Strategy Games

by Andrew Trusty, Santi Ontañón, Ashwin Ram

4th Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-08), Stanford, CA, October 2008

One response to this post.

  1. Posted by santi on October 13, 2009 at 5:33 pm

    Dan Kline, a Game AI programmer that has worked with companies such as Activision, Blizzard North, LucasArts, and Midway, and Head AI designer of the upcoming game “Star Wars: The Force Unleashed”, mentioned the presentation of this paper in his personal blog:


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