Learning User Evaluation Functions for Adaptive Scheduling Assistance
Abstract
In this paper, we describe Inca, an adaptive, advisable assistant for crisis response. The system lets users guide the search toward particular schedules by giving high-level, operational advice about the solutions desired. Because traces of user interactions provide information regarding the user's preferences among schedules, Inca can draw on machine learning techniques to construct user models that reflect these preferences. We characterize the modeling task as that of learning a weight vector for a linear evaluation function that will lead to the same pairwise preferences between schedules as the user. Inca adapts to individual users by adjusting the weights on its evaluation function using a perceptron-type learning algorithm. To evaluate the system's adaptive capabilities, we designed an experiment involving four types of synthetic users that differed in their evaluation functions and in the level of advice they provide. We present experimental results showi...
Cite
Text
Gervasio et al. "Learning User Evaluation Functions for Adaptive Scheduling Assistance." International Conference on Machine Learning, 1999.Markdown
[Gervasio et al. "Learning User Evaluation Functions for Adaptive Scheduling Assistance." International Conference on Machine Learning, 1999.](https://mlanthology.org/icml/1999/gervasio1999icml-learning/)BibTeX
@inproceedings{gervasio1999icml-learning,
title = {{Learning User Evaluation Functions for Adaptive Scheduling Assistance}},
author = {Gervasio, Melinda T. and Iba, Wayne and Langley, Pat},
booktitle = {International Conference on Machine Learning},
year = {1999},
pages = {152-161},
url = {https://mlanthology.org/icml/1999/gervasio1999icml-learning/}
}