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Cooperative Bayesian optimization for imperfect agents
Publikationstyp
Conference Paper
Date Issued
2023-09-17
Sprache
English
Author(s)
Khoshvishkaie, Ali
Kaski, Samuel
Volume
14169 LNAI
Start Page
475
End Page
490
Citation
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2023)
Publisher DOI
Scopus ID
Publisher
Springer Nature Switzerland
ISBN
978-3-031-43411-2
978-3-031-43412-9
978-3-031-43413-2
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.
DDC Class
004: Computer Sciences