Monitoring the Execution of Partial-Order Plans via Regression

Monitoring the Execution of Partial-Order Plans via Regression. Muise, C., McIlraith, S. A., & Beck, J. C. In International Joint Conference On Artificial Intelligence, 2011.

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Partial-order plans (POPs) have the capacity to compactly represent numerous distinct plan lin- earizations and as a consequence are inherently ro- bust. We exploit this robustness to do effective ex- ecutionmonitoring. We characterize the conditions underwhich a POP remains viable as the regression of the goal through the structure of a POP.We then develop a method for POP execution monitoring via a structured policy, expressed as an ordered al- gebraic decision diagram. The policy encompasses both state evaluation and action selection, enabling an agent to seamlessly switch between POP lin- earizations to accommodate unexpected changes during execution. We demonstrate the effective- ness of our approach by comparing it empirically and analytically to a standard technique for execu- tion monitoring of sequential plans. On standard benchmark planning domains, our approach is 2 to 17 times faster and up to 2.5 times more robust than comparable monitoring of a sequential plan. On POPs that have few ordering constraints among actions, our approach is significantly more robust, with the ability to continue executing in up to an exponential number of additional states.

@inproceedings{muise-ijcai11,
abstract = {Partial-order plans (POPs) have the capacity to compactly represent numerous distinct plan lin- earizations and as a consequence are inherently ro- bust. We exploit this robustness to do effective ex- ecutionmonitoring. We characterize the conditions underwhich a POP remains viable as the regression of the goal through the structure of a POP.We then develop a method for POP execution monitoring via a structured policy, expressed as an ordered al- gebraic decision diagram. The policy encompasses both state evaluation and action selection, enabling an agent to seamlessly switch between POP lin- earizations to accommodate unexpected changes during execution. We demonstrate the effective- ness of our approach by comparing it empirically and analytically to a standard technique for execu- tion monitoring of sequential plans. On standard benchmark planning domains, our approach is 2 to 17 times faster and up to 2.5 times more robust than comparable monitoring of a sequential plan. On POPs that have few ordering constraints among actions, our approach is significantly more robust, with the ability to continue executing in up to an exponential number of additional states.},
author = {Muise, Christian and McIlraith, Sheila A. and Beck, J. Christopher},
booktitle = {International Joint Conference On Artificial Intelligence},
series = {International Joint Conference On Artificial Intelligence},
title = {{Monitoring the Execution of Partial-Order Plans via Regression}},
year = {2011},
keywords = {plan execution},
url = {http://ijcai.org/papers11/Papers/IJCAI11-330.pdf}
}

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