Bayesian Interactive Decision Support for Multi-Attribute Problems with Even Swaps

Debarun Bhattacharjya, Jeffrey Kephart
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:892-901, 2014.

Abstract

Even swaps is a method for solving de- terministic multi-attribute decision problems where the decision maker iteratively simpli- fies the problem until the optimal alterna- tive is revealed (Hammond et al. 1998, 1999). We present a new practical decision support system that takes a Bayesian approach to guiding the even swaps process, where the system makes queries based on its beliefs about the decision maker’s preferences and updates them as the interactive process un- folds. Through experiments, we show that it is possible to learn enough about the decision maker’s preferences to measurably reduce the cognitive burden, i.e. the number and com- plexity of queries posed by the system.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-bhattacharjya14a, title = {{B}ayesian Interactive Decision Support for Multi-Attribute Problems with Even Swaps}, author = {Bhattacharjya, Debarun and Kephart, Jeffrey}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {892--901}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/bhattacharjya14a/bhattacharjya14a.pdf}, url = {https://proceedings.mlr.press/r12/bhattacharjya14a.html}, abstract = {Even swaps is a method for solving de- terministic multi-attribute decision problems where the decision maker iteratively simpli- fies the problem until the optimal alterna- tive is revealed (Hammond et al. 1998, 1999). We present a new practical decision support system that takes a Bayesian approach to guiding the even swaps process, where the system makes queries based on its beliefs about the decision maker’s preferences and updates them as the interactive process un- folds. Through experiments, we show that it is possible to learn enough about the decision maker’s preferences to measurably reduce the cognitive burden, i.e. the number and com- plexity of queries posed by the system.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Bayesian Interactive Decision Support for Multi-Attribute Problems with Even Swaps %A Debarun Bhattacharjya %A Jeffrey Kephart %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-bhattacharjya14a %I PMLR %P 892--901 %U https://proceedings.mlr.press/r12/bhattacharjya14a.html %V R12 %X Even swaps is a method for solving de- terministic multi-attribute decision problems where the decision maker iteratively simpli- fies the problem until the optimal alterna- tive is revealed (Hammond et al. 1998, 1999). We present a new practical decision support system that takes a Bayesian approach to guiding the even swaps process, where the system makes queries based on its beliefs about the decision maker’s preferences and updates them as the interactive process un- folds. Through experiments, we show that it is possible to learn enough about the decision maker’s preferences to measurably reduce the cognitive burden, i.e. the number and com- plexity of queries posed by the system. %Z Reissued by PMLR on 04 October 2026.
APA
Bhattacharjya, D. & Kephart, J.. (2014). Bayesian Interactive Decision Support for Multi-Attribute Problems with Even Swaps. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:892-901 Available from https://proceedings.mlr.press/r12/bhattacharjya14a.html. Reissued by PMLR on 04 October 2026.

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