Learning to select computations

Frederick Callaway, Sayan Gul, Paul Krueger, Thomas L. Griffiths, Falk Lieder

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

2 Scopus citations

Abstract

The efficient use of limited computational resources is an essential ingredient of intelligence. Selecting computations optimally according to rational metareasoning would achieve this, but this is computationally intractable. Inspired by psychology and neuroscience, we propose the first concrete and domain-general learning algorithm for approximating the optimal selection of computations: Bayesian metalevel policy search (BMPS). We derive this general, sample-efficient search algorithm for a computation-selecting metalevel policy based on the insight that the value of information lies between the myopic value of information and the value of perfect information. We evaluate BMPS on three increasingly difficult metareasoning problems: when to terminate computation, how to allocate computation between competing options, and planning. Across all three domains, BMPS achieved near-optimal performance and compared favorably to previously proposed metar-easoning heuristics. Finally, we demonstrate the practical utility of BMPS in an emergency management scenario, even accounting for the overhead of metareasoning.

Original languageEnglish (US)
Title of host publication34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018
EditorsRicardo Silva, Amir Globerson, Amir Globerson
PublisherAssociation For Uncertainty in Artificial Intelligence (AUAI)
Pages776-785
Number of pages10
ISBN (Electronic)9781510871601
StatePublished - Jan 1 2018
Externally publishedYes
Event34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018 - Monterey, United States
Duration: Aug 6 2018Aug 10 2018

Publication series

Name34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018
Volume2

Other

Other34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018
CountryUnited States
CityMonterey
Period8/6/188/10/18

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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  • Cite this

    Callaway, F., Gul, S., Krueger, P., Griffiths, T. L., & Lieder, F. (2018). Learning to select computations. In R. Silva, A. Globerson, & A. Globerson (Eds.), 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018 (pp. 776-785). (34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018; Vol. 2). Association For Uncertainty in Artificial Intelligence (AUAI).