A Behavior Language for Story-Based Believable Agents
IEEE Intelligent Systems
Automatically acquiring domain knowledge for adaptive game AI using evolutionary learning
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Journal of Artificial Intelligence Research
Generalizing plans to new environments in relational MDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Real-time strategy gaines: a new AI research challenge
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Conceptual Neighborhoods for Retrieval in Case-Based Reasoning
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
A data mining approach to strategy prediction
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Fuzzy case-based reasoning for managing strategic and tactical reasoning in starcraft
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
A novel agent based control scheme for RTS games
Proceedings of The 8th Australasian Conference on Interactive Entertainment: Playing the System
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We present a real-time strategy (RTS) game AI agent that integrates multiple specialist components to play a complete game. Based on an analysis of how skilled human players conceptualize RTS gameplay, we partition the problem space into domains of competence seen in expert human play. This partitioning helps us to manage and take advantage of the large amount of sophisticated domain knowledge developed by human players. We present results showing that incorporating expert high-level strategic knowledge allows our agent to consistently defeat established scripted AI players. In addition, this work lays the foundation to incorporate tactics and unit micromanagement techniques developed by both man and machine.