Empirical model-building and response surface
Empirical model-building and response surface
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
On the Efficient Allocation of Resources for Hypothesis Evaluation: A Statistical Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Adaptive problem-solving for large-scale scheduling problems: a case study
Journal of Artificial Intelligence Research
Efficient heuristic hypothesis ranking
Journal of Artificial Intelligence Research
Provably bounded optimal agents
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Guided restarting local search for production planning
Engineering Applications of Artificial Intelligence
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Optimization of expected values in a stochastic domain is common in real world applications. However, it is often difficult to solve such optimization problems without significant knowledge about the surface defined by the stochastic function. In this paper we examine the use of local search techniques to solve stochastic optimization. In particular, we analyze assumptions of smoothness upon which these approaches often rely. We examine these assumptions in the context of optimizing search heuristics for a planner/scheduler on two problem domains. We compare three search algorithms to improve the heuristic sets and show that the two chosen local search algorithms perform well. We present empirical data that suggests this is due to smoothness properties of the search space for the search algorithms.