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Experience in Engineering Complex Systems: Active Preference Learning With Multiple Outcomes and Certainty Levels
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AI
DOI：10.1109/THMS.2025.3617576.png)
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Abstract
En 中文
Black-box optimization involves solving optimization problems where the objective function and/or constraints are unknown, inaccessible, or do not explicitly exist. In many applications, particularly those involving human interaction, the optimization problem can only be accessed through physical experiments, with the available outcomes based on the preference of one candidate over one or more others. Accordingly, algorithms for active preference learning have been developed to exploit this specific information in constructing a surrogate of the objective function. This surrogate is then used to define an acquisition function that suggests new decision vectors to search for the optimal solution iteratively. Based on this idea, our approach aims to extend active preference learning algorithms to leverage further information effectively, which can be obtained in reality, such as: a five-point Likert-type scale for the outcomes of the preference query (i.e., the preference can be described not only as this is better than that but also as this is much better than that), or multiple outcomes for a single preference query with possible additive information on how certain the outcomes are. The validation of the proposed algorithm is done through some standard benchmark functions, and, in practice, through tuning parameters for robot sealing and human-robot collaboration experiments, showing a promising improvement with respect to the state-of-the-art algorithm in the same context.
Keywords:
Optimization
Robots
Linear programming
Uncertainty
Machine learning algorithms
Computational modeling
Collaboration
Closed box
Sealing materials
Accuracy
Human-robot interaction
optimization machine
learning algorithms
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