Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Machine learning from examples: inductive and lazy methods
Data & Knowledge Engineering - Special jubilee issue: DKE 25
ACM Computing Surveys (CSUR)
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Applications of Learning Classifier Systems
Applications of Learning Classifier Systems
Fast Branch & Bound Algorithms for Optimal Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive game AI with dynamic scripting
Machine Learning
AI Game Development
Case-Based Planning and Execution for Real-Time Strategy Games
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Transfer learning in real-time strategy games using hybrid CBR/RL
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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
Evolutionary computation and games
IEEE Computational Intelligence Magazine
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
K-winner machines for pattern classification
IEEE Transactions on Neural Networks
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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The underlying goal of a competing agent in a discrete real-time strategy (RTS) game is to defeat an adversary. Strategic agents or participants must define an a priori plan to maneuver their resources in order to destroy the adversary and the adversary's resources as well as secure physical regions of the environment. This a priori plan can be generated by leveraging collected historical knowledge about the environment. This knowledge is then employed in the generation of a classification model for real-time decision-making in the RTS domain. The best way to generate a classification model for a complex problem domain depends on the characteristics of the solution space. An experimental method to determine solution space (search landscape) characteristics is through analysis of historical algorithm performance for solving the specific problem. We select a deterministic search technique and a stochastic search method for a priori classification model generation. These approaches are designed, implemented, and tested for a specific complex RTS game, Bos Wars. Their performance allows us to draw various conclusions about applying a competing agent in complex search landscapes associated with RTS games.