Modeling and prediction of human behavior
Neural Computation
Multiple Adaptive Agents for Tactical Driving
Applied Intelligence
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Action recognition and prediction for driver assistance systems using dynamic belief networks
NODe'02 Proceedings of the NODe 2002 agent-related conference on Agent technologies, infrastructures, tools, and applications for E-services
Evolution Strategies for Direct Policy Search
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Variable Metric Reinforcement Learning Methods Applied to the Noisy Mountain Car Problem
Recent Advances in Reinforcement Learning
Hoeffding and Bernstein races for selecting policies in evolutionary direct policy search
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Uncertainty handling CMA-ES for reinforcement learning
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Neuroevolution strategies for episodic reinforcement learning
Journal of Algorithms
Evolutionary optimization of dynamics models in sequential Monte Carlo target tracking
IEEE Transactions on Evolutionary Computation
Engineering Applications of Artificial Intelligence
A simulation environment for analysis and optimization of driver models
ICDHM'11 Proceedings of the Third international conference on Digital human modeling
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Originally developed to generate behavior in autonomous robots, attractor dynamics encode basic behavioral tendencies with meaningful parameters that support optimizations through direct policy search. We combined attractor dynamics with a powerful evolutionary algorithm to arrive at driver models that capture behavioral patterns of real human drivers who employ various driving styles. These models support the development of driver assistance systems in several ways.