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Landmark-based heuristic online contingent planning

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Abstract

In contingent planning problems, agents have partial information about their state and use sensing actions to learn the value of some variables. When sensing and actuation are separated, plans for such problems can often be viewed as a tree of sensing actions, separated by conformant plans consisting of non-sensing actions that enable the execution of the next sensing action. We propose a heuristic, online method for contingent planning which focuses on identifying the next useful sensing action. We select the next sensing action based on a landmark heuristic, adapted from classical planning. We discuss landmarks for plan trees, providing several alternative definitions and discussing their merits. The key part of our planner is the novel landmarks-based heuristic, together with a projection method that uses classical planning to solve the intermediate conformant planning problems. The resulting heuristic contingent planner solves many more problems than state-of-the-art, translation-based online contingent planners, and in most cases, much faster, up to 3 times faster on simple problems, and 200 times faster on non-simple domains.

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Notes

  1. One can define \(\exists \) knowledge landmarks, but as we explained above, these are of limited usefulness in our online planning case.

  2. The term complete is justified here only because we have assumed that action preconditions and goals are conjunctions of literals.

  3. This generalization was not implemented.

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Acknowledgements

We thank the reviewers for their useful comments. This work was supported by ISF Grant 933/13, by the Israel Ministry of Science and Technology Grant 54178, by the Helmsley Charitable Trust through the Agricultural, Biological and Cognitive Robotics Center of Ben-Gurion University of the Negev, and by the Lynn and William Frankel Center for Computer Science.

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Authors and Affiliations

  1. Information Systems Engineering, Ben Gurion University of the Negev, Beer-Sheva, Israel

    Shlomi Maliah & Guy Shani

  2. Computer Science Department, Ben Gurion University of the Negev, Beer-Sheva, Israel

    Ronen I. Brafman

Authors
  1. Shlomi Maliah
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  2. Guy Shani
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  3. Ronen I. Brafman
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Correspondence to Guy Shani.

Additional information

Parts of this paper appeared in [16].

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Maliah, S., Shani, G. & Brafman, R.I. Landmark-based heuristic online contingent planning. Auton Agent Multi-Agent Syst 32, 602–634 (2018). https://doi.org/10.1007/s10458-018-9389-9

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