Progress in Artificial Intelligence

, Volume 6, Issue 4, pp 299–314 | Cite as

Incremental contingency planning for recovering from critical outcomes in high-probability seed plans

  • Yolanda E-Martín
  • María D. R-Moreno
  • David E. Smith
Regular Paper

Abstract

Planning is the problem of choosing and organizing a sequence of actions that when applied in a given initial state results in a goal state. However, in real problems unexpected action outcomes may occur and the initial state of the world may not be known with certainty. Incremental contingency planning considers potential failures in a plan and attempts to avoid them by incrementally adding contingency branches to the plan in order to improve the overall probability. The planner focuses on high-probability outcomes and attempts to avoid them by incrementally adding contingency branches to the plan in order to improve the overall probability. Some of these high-probability outcomes might be repairable by runtime replanning so we focus on repairing critical outcomes that cannot be fixed by runtime replanning. For this planning to be successful, we also need high-probability seed plans. In this work, we describe approaches to generating high-probability seed plans and to incremental contingency planning on the critical outcomes.

Keywords

Probabilistic planning Plan graph propagation Probability interaction Heuristic search 

Notes

Acknowledgements

This work was supported by the NASA Safe Autonomous Systems Operations (SASO) project, the MINECO project EphemeCH TIN2014-56494-C4-4-P, and the UAH project 2016/00351/001.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Yolanda E-Martín
    • 1
  • María D. R-Moreno
    • 2
  • David E. Smith
    • 3
  1. 1.Universidad Carlos III de MadridLeganésSpain
  2. 2.Universidad de AlcaláAlcalá de HenaresSpain
  3. 3.NASA Ames Research CenterMoffett FieldUSA

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