Representing and Solving Asymmetric Decision Problems Using Valuation Networks

Prakash P. Shenoy
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:488-494, 1995.

Abstract

In this paper, we investigate the use of valuation networks to represent and solve asymmetric decision problems. The structural asymmetry information is represented by indicator valuations. An indicator valuation is a special type of a probability valuation whose values are restricted to either 0 or 1 . Indicator valuations enable us to reduce the domain of probability valuations and this contributes greatly to improving the computational efficiency of the solution technique. We use indicator valuations to define effective frames as subsets of frames of variables. All numeric information is specified only for effective frames. The solution technique is mostly the same as in the symmetric case. The main difference is that all computations are done on the effective frames of variables. This contributes to the increased efficiency of the solution technique. Also, when restricted to effective frames, the values of indicator valuations are identically one, and therefore indicator valuations can be handled implicitly and this contributes further to the increased efficiency of the solution technique.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR0-shenoy95a, title = {Representing and Solving Asymmetric Decision Problems Using Valuation Networks}, author = {Shenoy, Prakash P.}, booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics}, pages = {488--494}, year = {1995}, editor = {Fisher, Doug and Lenz, Hans-Joachim}, volume = {R0}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/r0/shenoy95a/shenoy95a.pdf}, url = {https://proceedings.mlr.press/r0/shenoy95a.html}, abstract = {In this paper, we investigate the use of valuation networks to represent and solve asymmetric decision problems. The structural asymmetry information is represented by indicator valuations. An indicator valuation is a special type of a probability valuation whose values are restricted to either 0 or 1 . Indicator valuations enable us to reduce the domain of probability valuations and this contributes greatly to improving the computational efficiency of the solution technique. We use indicator valuations to define effective frames as subsets of frames of variables. All numeric information is specified only for effective frames. The solution technique is mostly the same as in the symmetric case. The main difference is that all computations are done on the effective frames of variables. This contributes to the increased efficiency of the solution technique. Also, when restricted to effective frames, the values of indicator valuations are identically one, and therefore indicator valuations can be handled implicitly and this contributes further to the increased efficiency of the solution technique.}, note = {Reissued by PMLR on 01 May 2022.} }
Endnote
%0 Conference Paper %T Representing and Solving Asymmetric Decision Problems Using Valuation Networks %A Prakash P. Shenoy %B Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1995 %E Doug Fisher %E Hans-Joachim Lenz %F pmlr-vR0-shenoy95a %I PMLR %P 488--494 %U https://proceedings.mlr.press/r0/shenoy95a.html %V R0 %X In this paper, we investigate the use of valuation networks to represent and solve asymmetric decision problems. The structural asymmetry information is represented by indicator valuations. An indicator valuation is a special type of a probability valuation whose values are restricted to either 0 or 1 . Indicator valuations enable us to reduce the domain of probability valuations and this contributes greatly to improving the computational efficiency of the solution technique. We use indicator valuations to define effective frames as subsets of frames of variables. All numeric information is specified only for effective frames. The solution technique is mostly the same as in the symmetric case. The main difference is that all computations are done on the effective frames of variables. This contributes to the increased efficiency of the solution technique. Also, when restricted to effective frames, the values of indicator valuations are identically one, and therefore indicator valuations can be handled implicitly and this contributes further to the increased efficiency of the solution technique. %Z Reissued by PMLR on 01 May 2022.
APA
Shenoy, P.P.. (1995). Representing and Solving Asymmetric Decision Problems Using Valuation Networks. Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R0:488-494 Available from https://proceedings.mlr.press/r0/shenoy95a.html. Reissued by PMLR on 01 May 2022.

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