Random generation of combinatorial structures from a uniform
Theoretical Computer Science
Heuristic sampling on backtrack trees
Heuristic sampling on backtrack trees
Systematic and nonsystematic search strategies
Proceedings of the first international conference on Artificial intelligence planning systems
Algorithms for random generation and counting: a Markov chain approach
Algorithms for random generation and counting: a Markov chain approach
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Branch and bound algorithm selection by performance prediction
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Automatic generation of heuristics for scheduling
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Heuristic-biased stochastic sampling
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Predicting the size of IDA*'s search tree
Artificial Intelligence
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This paper presents the Expected Solution Quality (ESQ) method for statistically characterizing scheduling problems and the performance of schedulers. The ESQ method is demonstrated by applying it to a practical telescope scheduling problem. The method addresses the important and difficult issue of how to meaningfully evaluate the performance of a scheduler on a constrained optimization problem for which an optimal solution is not known. At the heart of ESQ is a Monte Carlo algor ithm that estimates a problem's probability density function with respect to solution quality This "quality density function" provides a useful characterization of a scheduling problem, and it also provides a background against which scheduler performance can be meaningfully evaluated. ESQ provides a unitless measure that combines both schedule quality and the amount of time to generate a schedule.