First-order open-universe POMDPs

Siddharth Srivastava, Stuart Russell, Paul Ruan, Xiang Cheng

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

12 Scopus citations

Abstract

Open-universe probability models, representable by a variety of probabilistic programming languages (PPLs), handle uncertainty over the existence and identity of objects-forms of uncertainty occurring in many real-world situations. We examine the problem of extending a declarative PPL to define decision problems (specifically, POMDPs) and identify non-trivial representational issues in describing an agent's capability for observation and action-issues that were avoided in previous work only by making strong and restrictive assumptions. We present semantic definitions that lead to POMDP specifications provably consistent with the sensor and actuator capabilities of the agent. We also describe a variant of point-based value iteration for solving open-universe POMDPs. Thus, we handle cases-such as seeing a new object and picking it up-that could not previously be represented or solved.

Original languageEnglish (US)
Title of host publicationUncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014
EditorsNevin L. Zhang, Jin Tian
PublisherAUAI Press
Pages742-751
Number of pages10
ISBN (Electronic)9780974903910
StatePublished - Jan 1 2014
Externally publishedYes
Event30th Conference on Uncertainty in Artificial Intelligence, UAI 2014 - Quebec City, Canada
Duration: Jul 23 2014Jul 27 2014

Publication series

NameUncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014

Other

Other30th Conference on Uncertainty in Artificial Intelligence, UAI 2014
CountryCanada
CityQuebec City
Period7/23/147/27/14

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Srivastava, S., Russell, S., Ruan, P., & Cheng, X. (2014). First-order open-universe POMDPs. In N. L. Zhang, & J. Tian (Eds.), Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014 (pp. 742-751). (Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014). AUAI Press.