A batch Bayesian approach for bilevel multi-objective decision making under uncertainty
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Accepted version
Date
2023-02-13
Authors
Dogan, Vedat
Prestwich, Steven D.
Journal Title
Journal ISSN
Volume Title
Publisher
Association for the Advancement of Artificial Intelligence; AAAI
Published Version
Abstract
Bilevel multiobjective optimization is a field of mathematical programming representing a nested hierarchical decision making process, with one or more decision makers at each level. These problems appear in many practical applications, solving tasks such as optimal control, process optimization, governmental and game playing strategy development, and transportation. Uncertainty cannot be ignored in these practical problems. We present a hybrid algorithm called BAM- BINO, based on a batch Bayesian approach via expected hyper-volume improvement, that can handle uncertainty at the upper level. Three popular modified benchmark problems with multiple dimensions are used to evaluate its performance under objective noise compared to two popular algorithms in the literature. The results show that BAMBINO is computationally efficient and able to handle upper level uncertainty. We also evaluate the effect of batch size on performance.
Description
Keywords
Bayesian optimization , Bilevel optimization problems , Multi-objective acquisition , Multi-objective optimization , BAMBINO , Uncertainty , Batch Bayesian approach
Citation
Dogan, V. and Prestwich, S. D. (2023) 'A batch Bayesian approach for bilevel multi-objective decision making under uncertainty', AAAI 23: Thirty-Seventh AAAI Conference on Artificial Intelligence, 1st AAAI Workshop on Uncertainty Reasoning and Quantification in Decision Making, 7-14 February, Washington DC, USA.