Some domains, such as real-time strategy (RTS) games, pose several challenges to traditional planning and machine learning techniques. In this paper, we present a novel on-line case-based planning architecture that addresses some of these problems. Our architecture addresses issues of plan acquisition, on-line plan execution, interleaved planning and execution and on-line plan adaptation. We also introduce the Darmok system, which implements this architecture in order to play Wargus (an open source clone of the well-known RTS game Warcraft II). We present empirical evaluation of the performance of Darmok and show that it successfully learns to play the Wargus game.
Read the paper:
On-Line Case-Based Planning
by Santi Ontañón, Neha Sugandh, Kinshuk Mishra, Ashwin Ram
Computational Intelligence, 26(1):84-119, 2010.www.cc.gatech.edu/faculty/ashwin/papers/er-09-08.pdf www3.interscience.wiley.com/journal/123263882/abstract
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Cognitive Computing 
Posted by Ahmad Atta on November 3, 2009 at 12:11 pm
Great Paper ! It is very useful for my graduation project, and I learn more and more from it.
I really like your way of writing, your examples in this paper and others help me know how to apply the CBR concepts.
Many thanks 🙂
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