Synthesizing robust plans under incomplete domain models

Tuan A. Nguyen, Subbarao Kambhampati, Minh Do

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

Most current planners assume complete domain models and focus on generating correct plans. Unfortunately, domain modeling is a laborious and error-prone task, thus real world agents have to plan with incomplete domain models. While domain experts cannot guarantee completeness, often they are able to circumscribe the incompleteness of the model by providing annotations as to which parts of the domain model may be incomplete. In such cases, the goal should be to synthesize plans that are robust with respect to any known incompleteness of the domain. In this paper, we first introduce annotations expressing the knowledge of the domain incompleteness and formalize the notion of plan robustness with respect to an incomplete domain model. We then show an approach to compiling the problem of finding robust plans to the conformant probabilistic planning problem, and present experimental results with Probabilistic-FF planner.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems
PublisherNeural information processing systems foundation
StatePublished - 2013
Event27th Annual Conference on Neural Information Processing Systems, NIPS 2013 - Lake Tahoe, NV, United States
Duration: Dec 5 2013Dec 10 2013

Other

Other27th Annual Conference on Neural Information Processing Systems, NIPS 2013
CountryUnited States
CityLake Tahoe, NV
Period12/5/1312/10/13

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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  • Cite this

    Nguyen, T. A., Kambhampati, S., & Do, M. (2013). Synthesizing robust plans under incomplete domain models. In Advances in Neural Information Processing Systems Neural information processing systems foundation.