Dynamic programming in influence diagrams with decision circuits

Ross Shachter, Debarun Bhattacharjya
Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:508-515, 2010.

Abstract

Decision circuits perform efficient evaluation of influence diagrams, building on the ad- vances in arithmetic circuits for belief net- work inference [Darwiche, 2003; Bhattachar- jya and Shachter, 2007]. We show how even more compact decision circuits can be con- structed for dynamic programming in influ- ence diagrams with separable value functions and conditionally independent subproblems. Once a decision circuit has been constructed based on the diagram’s “global” graphical structure, it can be compiled to exploit “lo- cal” structure for efficient evaluation and sen- sitivity analysis.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR8-shachter10a, title = {Dynamic programming in influence diagrams with decision circuits}, author = {Shachter, Ross and Bhattacharjya, Debarun}, booktitle = {Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence}, pages = {508--515}, year = {2010}, editor = {Grünwald, Peter and Spirtes, Peter}, volume = {R8}, series = {Proceedings of Machine Learning Research}, month = {08--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r8/main/assets/shachter10a/shachter10a.pdf}, url = {https://proceedings.mlr.press/r8/shachter10a.html}, abstract = {Decision circuits perform efficient evaluation of influence diagrams, building on the ad- vances in arithmetic circuits for belief net- work inference [Darwiche, 2003; Bhattachar- jya and Shachter, 2007]. We show how even more compact decision circuits can be con- structed for dynamic programming in influ- ence diagrams with separable value functions and conditionally independent subproblems. Once a decision circuit has been constructed based on the diagram’s “global” graphical structure, it can be compiled to exploit “lo- cal” structure for efficient evaluation and sen- sitivity analysis.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Dynamic programming in influence diagrams with decision circuits %A Ross Shachter %A Debarun Bhattacharjya %B Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2010 %E Peter Grünwald %E Peter Spirtes %F pmlr-vR8-shachter10a %I PMLR %P 508--515 %U https://proceedings.mlr.press/r8/shachter10a.html %V R8 %X Decision circuits perform efficient evaluation of influence diagrams, building on the ad- vances in arithmetic circuits for belief net- work inference [Darwiche, 2003; Bhattachar- jya and Shachter, 2007]. We show how even more compact decision circuits can be con- structed for dynamic programming in influ- ence diagrams with separable value functions and conditionally independent subproblems. Once a decision circuit has been constructed based on the diagram’s “global” graphical structure, it can be compiled to exploit “lo- cal” structure for efficient evaluation and sen- sitivity analysis. %Z Reissued by PMLR on 04 October 2026.
APA
Shachter, R. & Bhattacharjya, D.. (2010). Dynamic programming in influence diagrams with decision circuits. Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R8:508-515 Available from https://proceedings.mlr.press/r8/shachter10a.html. Reissued by PMLR on 04 October 2026.

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