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 deterministic multi-attribute decision problems where the decision maker iteratively simplifies the problem until the optimal alternative 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 unfolds. 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 complexity 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 deterministic multi-attribute decision problems where the decision maker iteratively simplifies the problem until the optimal alternative 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 unfolds. 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 complexity 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 deterministic multi-attribute decision problems where the decision maker iteratively simplifies the problem until the optimal alternative 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 unfolds. 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 complexity 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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