Learning to select computations

Frederick Callaway, Sayan Gul, Paul M. Krueger, Thomas L. Griffiths, Falk Lieder
Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:775-784, 2018.

Abstract

The efficient use of limited computational re- sources is an essential ingredient of intel- ligence. Selecting computations optimally according to rational metareasoning would achieve this, but this is computationally in- tractable. Inspired by psychology and neu- roscience, we propose the first concrete and domain-general learning algorithm for approx- imating the optimal selection of computations: Bayesian metalevel policy search (BMPS). We derive this general, sample-efficient search al- gorithm 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 in- formation. We evaluate BMPS on three in- creasingly difficult metareasoning problems: when to terminate computation, how to allo- cate computation between competing options, and planning. Across all three domains, BMPS achieved near-optimal performance and com- pared 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR16-callaway18a, title = {Learning to select computations}, author = {Callaway, Frederick and Gul, Sayan and Krueger, Paul M. and Griffiths, Thomas L. and Lieder, Falk}, booktitle = {Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence}, pages = {775--784}, year = {2018}, editor = {Globerson, Amir and Silva, Ricardo}, volume = {R16}, series = {Proceedings of Machine Learning Research}, month = {06--10 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r16/main/assets/callaway18a/callaway18a.pdf}, url = {https://proceedings.mlr.press/r16/callaway18a.html}, abstract = {The efficient use of limited computational re- sources is an essential ingredient of intel- ligence. Selecting computations optimally according to rational metareasoning would achieve this, but this is computationally in- tractable. Inspired by psychology and neu- roscience, we propose the first concrete and domain-general learning algorithm for approx- imating the optimal selection of computations: Bayesian metalevel policy search (BMPS). We derive this general, sample-efficient search al- gorithm 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 in- formation. We evaluate BMPS on three in- creasingly difficult metareasoning problems: when to terminate computation, how to allo- cate computation between competing options, and planning. Across all three domains, BMPS achieved near-optimal performance and com- pared 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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Learning to select computations %A Frederick Callaway %A Sayan Gul %A Paul M. Krueger %A Thomas L. Griffiths %A Falk Lieder %B Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2018 %E Amir Globerson %E Ricardo Silva %F pmlr-vR16-callaway18a %I PMLR %P 775--784 %U https://proceedings.mlr.press/r16/callaway18a.html %V R16 %X The efficient use of limited computational re- sources is an essential ingredient of intel- ligence. Selecting computations optimally according to rational metareasoning would achieve this, but this is computationally in- tractable. Inspired by psychology and neu- roscience, we propose the first concrete and domain-general learning algorithm for approx- imating the optimal selection of computations: Bayesian metalevel policy search (BMPS). We derive this general, sample-efficient search al- gorithm 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 in- formation. We evaluate BMPS on three in- creasingly difficult metareasoning problems: when to terminate computation, how to allo- cate computation between competing options, and planning. Across all three domains, BMPS achieved near-optimal performance and com- pared 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. %Z Reissued by PMLR on 04 October 2026.
APA
Callaway, F., Gul, S., Krueger, P.M., Griffiths, T.L. & Lieder, F.. (2018). Learning to select computations. Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R16:775-784 Available from https://proceedings.mlr.press/r16/callaway18a.html. Reissued by PMLR on 04 October 2026.

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