Solving large TÆMS problems efficiently by selective exploration and decomposition

@inproceedings{Wu2007SolvingLT,
  title={Solving large T{\aE}MS problems efficiently by selective exploration and decomposition},
  author={Jianhui Wu and Edmund H. Durfee},
  booktitle={AAMAS},
  year={2007}
}
TÆMS is a hierarchical modeling language capable of representing complex task networks with intra-task uncertainties and inter-task dependencies. The uncertainty and complexity of the application domains represented in TÆMS models often lead to very large state spaces, which push the need to design efficient solution algorithms for TÆMS problems. In this paper, we present a solver that integrates selective state space search techniques with state space decomposition techniques. Our experiments… CONTINUE READING

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