Cooperative Bayesian Optimization for Imperfect Agents
Abstract
We introduce a cooperative Bayesian optimization problem for optimizing black-box functions of two variables where two agents choose together at which points to query the function but have only control over one variable each. This setting is inspired by human-AI teamwork, where an AI-assistant helps its human user solve a problem, in this simplest case, collaborative optimization. We formulate the solution as sequential decision-making, where the agent we control models the user as a computationally rational agent with prior knowledge about the function. We show that strategic planning of the queries enables better identification of the global maximum of the function as long as the user avoids excessive exploration. This planning is made possible by using Bayes Adaptive Monte Carlo planning and by endowing the agent with a user model that accounts for conservative belief updates and exploratory sampling of the points to query.
Cite
Text
Khoshvishkaie et al. "Cooperative Bayesian Optimization for Imperfect Agents." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2023. doi:10.1007/978-3-031-43412-9_28Markdown
[Khoshvishkaie et al. "Cooperative Bayesian Optimization for Imperfect Agents." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2023.](https://mlanthology.org/ecmlpkdd/2023/khoshvishkaie2023ecmlpkdd-cooperative/) doi:10.1007/978-3-031-43412-9_28BibTeX
@inproceedings{khoshvishkaie2023ecmlpkdd-cooperative,
title = {{Cooperative Bayesian Optimization for Imperfect Agents}},
author = {Khoshvishkaie, Ali and Mikkola, Petrus and Murena, Pierre-Alexandre and Kaski, Samuel},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2023},
pages = {475-490},
doi = {10.1007/978-3-031-43412-9_28},
url = {https://mlanthology.org/ecmlpkdd/2023/khoshvishkaie2023ecmlpkdd-cooperative/}
}