Cooperative Bayesian Optimization for Imperfect Agents

Ali Khoshvishkaie, Petrus Mikkola, Samuel Kaski, Pierre Alexandre Murena

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.
Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases : Research Track - European Conference, ECML PKDD 2023, Proceedings
EditorsDanai Koutra, Claudia Plant, Manuel Gomez Rodrigues, Elena Baralis, Francesco Bronchi
PublisherSpringer Nature
Pages475-490
Number of pages16
Volume14169 LNAI
ISBN (Electronic)1611-3349
ISBN (Print)978-3-031-43411-2
Publication statusPublished - 17 Sept 2023

Keywords

  • Bayesian optimization

Research Beacons, Institutes and Platforms

  • Digital Futures
  • Institute for Data Science and AI
  • Christabel Pankhurst Institute

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