Bayesian Interactive Decision Support for Multi-Attribute Problems with Even Swaps
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. 1
Cite
Text
Bhattacharjya and Kephart. "Bayesian Interactive Decision Support for Multi-Attribute Problems with Even Swaps." Conference on Uncertainty in Artificial Intelligence, 2014.Markdown
[Bhattacharjya and Kephart. "Bayesian Interactive Decision Support for Multi-Attribute Problems with Even Swaps." Conference on Uncertainty in Artificial Intelligence, 2014.](https://mlanthology.org/uai/2014/bhattacharjya2014uai-bayesian/)BibTeX
@inproceedings{bhattacharjya2014uai-bayesian,
title = {{Bayesian Interactive Decision Support for Multi-Attribute Problems with Even Swaps}},
author = {Bhattacharjya, Debarun and Kephart, Jeffrey O.},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2014},
pages = {72-81},
url = {https://mlanthology.org/uai/2014/bhattacharjya2014uai-bayesian/}
}