Emerging domains such as smart electric grids require decisions to be made autonomously, based on the observed behaviors of large numbers of connected consumers. Existing approaches either lack the flexibility to capture nuanced, individualized preference profiles, or scale poorly with the size of the dataset. We propose a preference model that combines flexible Bayesian nonparametric priors—providing state-of-the-art predictive power—with well-justified structural assumptions that allow a scalable implementation. The Gaussian process scalable preference model via Kronecker factorization (GaSPK) model provides accurate choice predictions and principled uncertainty estimates as input to decision-making tasks. In consumer choice settings where alternatives are described by few key attributes, inference in our model is highly efficient and scalable to tens of thousands of choices.

Autonomous agents, Autonomous decision-making, Bayesian inference, Discrete choice, Gaussian processes, Laplace inference, Preferences
dx.doi.org/10.1007/s10994-018-5705-5, hdl.handle.net/1765/106185
ERIM Top-Core Articles
Machine Learning
Rotterdam School of Management (RSM), Erasmus University

Peters, M, Saar-Tsechansky, M, Ketter, W, Williamson, S.A. (Sinead A.), Groot, P. (Perry), & Heskes, T. (Tom). (2018). A scalable preference model for autonomous decision-making. Machine Learning, 1–30. doi:10.1007/s10994-018-5705-5