Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
A Bayesian model of plan recognition
Artificial Intelligence
Bayesian Models for Keyhole Plan Recognition in an Adventure Game
User Modeling and User-Adapted Interaction
Techniques for Plan Recognition
User Modeling and User-Adapted Interaction
Computational Statistics & Data Analysis - Nonlinear methods and data mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management 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
An integrated agent for playing real-time strategy games
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
UCT for tactical assault planning in real-time strategy games
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Case-based plan recognition in computer games
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Learning to win: case-based plan selection in a real-time strategy game
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Data cracker: developing a visual game analytic tool for analyzing online gameplay
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Data analytics for game development (NIER track)
Proceedings of the 33rd International Conference on Software Engineering
A data-driven approach for resource gathering in real-time strategy games
ADMI'11 Proceedings of the 7th international conference on Agents and Data Mining Interaction
Proceedings of the 9th conference on Computing Frontiers
Transactions on Compuational Collective Intelligence VI
Empirical analysis of user data in game software development
Proceedings of the ACM-IEEE international symposium on Empirical software engineering and measurement
Prediction of early stage opponents strategy for StarCraft AI using scouting and machine learning
Proceedings of the Workshop at SIGGRAPH Asia
Bayesian networks for micromanagement decision imitation in the RTS game starcraft
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
Proceedings of the 11th Annual Workshop on Network and Systems Support for Games
Online behavior change detection in computer games
Expert Systems with Applications: An International Journal
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We present a data mining approach to opponent modeling in strategy games. Expert gameplay is learned by applying machine learning techniques to large collections of game logs. This approach enables domain independent algorithms to acquire domain knowledge and perform opponent modeling. Machine learning algorithms are applied to the task of detecting an opponent's strategy before it is executed and predicting when an opponent will perform strategic actions. Our approach involves encoding game logs as a feature vector representation, where each feature describes when a unit or building type is first produced. We compare our representation to a state lattice representation in perfect and imperfect information environments and the results show that our representation has higher predictive capabilities and is more tolerant of noise. We also discuss how to incorporate our data mining approach into a full game playing agent.