Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Fuzzy and Neural Approaches in Engineering
Fuzzy and Neural Approaches in Engineering
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
Fighting Bloat with Nonparametric Parsimony Pressure
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Co-evolutionary Data Mining to Discover Rules for Fuzzy Resource Management
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Data Mining for Fuzzy Decision Tree Structure with a Genetic Program
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Guiding genetic program based data mining using fuzzy rules
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
A fuzzy logic based efficient energy saving approach for domestic heating systems
Integrated Computer-Aided Engineering
The spatial pheromone signal for ant colony optimisation
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Enhanced probabilistic neural network with local decision circles: A robust classifier
Integrated Computer-Aided Engineering
Causally-guided evolutionary optimization and its application to antenna array design
Integrated Computer-Aided Engineering
Teleoperation of multi-agent systems with nonuniform control input delays
Integrated Computer-Aided Engineering
Comparison of entity with fuzzy data types in fuzzy object-oriented databases
Integrated Computer-Aided Engineering
Integrated Computer-Aided Engineering
Conceptual design of object-oriented databases for fuzzy engineering information modeling
Integrated Computer-Aided Engineering
Extending engineering data model for web-based fuzzy information modeling
Integrated Computer-Aided Engineering
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Advances in a fuzzy decision theory that allow automatic cooperation between unmanned aerial vehicles (UAVs) are discussed. The algorithms determine points the UAVs are to sample, flight paths, and the optimal UAVs for the task and related changes during the mission. Human intervention is not required after the mission begins. The algorithms take into account what is known before and during the mission about UAV reliability, fuel, and kinematics as well as the measurement space's meteorological states, terrain, air traffic, threats and related uncertainties. The fuzzy decision tree for path assignment is a significant advance over an older fuzzy decision rule that was previously introduced. Simulations show the ability of the control algorithm to allow UAVs to effectively cooperate to increase the UAV team's likelihood of successfully measuring the atmospheric index of refraction over a large volume. A genetic program (GP) based data mining procedure is discussed for automatically evolving fuzzy decision trees. The GP is used to automatically create the fuzzy decision tree for real-time UAV path assignments. The GP based procedure offers several significant advances over previously introduced GP based data mining procedures. These advances help produce mathematically concise fuzzy decision trees that are consistent with human intuition.