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.
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Notes
- 1.
Implementation of our method and source code for the experiments are available at https://github.com/ChessGeek95/AI-assisted-Bayesian-optimization/.
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Acknowledgements
This research was supported by EU Horizon 2020 (HumanE AI NET, 952026) and UKRI Turing AI World-Leading Researcher Fellowship (EP/W002973/1). Computational resources were provided by the Aalto Science-IT project from Computer Science IT. The authors would like to thank Prof. Frans Oliehoek and Dr. Mert Celikok for their help in setting up the project and the reviewers for their insightful comments.
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Khoshvishkaie, A., Mikkola, P., Murena, PA., Kaski, S. (2023). Cooperative Bayesian Optimization for Imperfect Agents. In: Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14169. Springer, Cham. https://doi.org/10.1007/978-3-031-43412-9_28
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