Learning by Observing: Case-Based Decision Making in Complex Strategy Games

Authors:
Darko Obradovič;Armin Stahl
Affiliations:
German Research Center for Artificial Intelligence (DFKI) & Technical University of Kaiserslautern,;German Research Center for Artificial Intelligence (DFKI) & Technical University of Kaiserslautern,
Venue:
KI '08 Proceedings of the 31st annual German conference on Advances in Artificial Intelligence
Year:
2008

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Abstract

There is a growing research interest in the design of competitive and adaptive Game AI for complex computer strategy games. In this paper, we present a novel approach for developing intelligent bots, which is based on the idea to observe successful human players and to learn from their individual decisions and strategies. These decisions are then reused by a bot in similar situations, resulting in a flexible and realistic strategic behaviour with low development and knowledge acquisition costs. Using Case-Based Reasoning (CBR) techniques, we implement this principle in the Cyborgsystem and achieve to outperform scripted opponents in a challenging multiplayer scenario.