Solving Large TÆMS Problems Efficiently by Selective Exploration and Decomposition

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 efiient 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 demonstrate that the solver can find an (approximately) optimal solution much faster than prior approaches.

Authors

• Jianhui Wu
• Edmund H. Durfee

Keywords

• TÆMS model
• MDP
• selective exploration
• informed unrolling
• decomposition
• mission phasing