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Interactive Machine Learning (IML) seeks to integrate human expertise intomachine learning processes. However, most existing algorithms cannot be appliedto Realworld Scenarios because their state spaces and/or action spaces arelimited to discrete values. Furthermore, the interaction of all existingmethods is restricted to deciding between multiple proposals. We thereforepropose a novel framework based on Bayesian Optimization (BO). InteractiveBayesian Optimization (IBO) enables collaboration between machine learningalgorithms and humans. This framework captures user preferences and provides aninterface for users to shape the strategy by hand. Additionally, we'veincorporated a new acquisition function, Preference Expected Improvement (PEI),to refine the system's efficiency using a probabilistic model of the userpreferences. Our approach is geared towards ensuring that machines can benefitfrom human expertise, aiming for a more aligned and effective learning process.In the course of this work, we applied our method to simulations and in a realworld task using a Franka Panda robot to show human-robot collaboration.

Integrating Human Expertise in Continuous Spaces: A Novel Interactive Bayesian Optimization Framework with Preference Expected Improvement