BOBA：通过贝叶斯主动推断实现动态贝叶斯优化
BOBA: Dynamic Bayesian Optimization through Bayesian Active Inference
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- University College London（伦敦大学学院）
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中文总结 AI 辅助
针对动态黑箱优化中现有方法忽视时间变化的问题，提出基于自由能原理的采集函数BOBA，通过最小化未来状态预测不确定性，在查询受限设置中显著降低遗憾值。
中文摘要 AI 辅助
动态黑箱优化对贝叶斯优化（BO）提出了重大挑战，因为目标函数随时间演变，导致最优位置持续移动。现有的动态贝叶斯优化（DBO）方法使用标准采集函数（如置信上界（UCB））未能显式考虑时间变化，导致样本分配次优以及对移动最优点的跟踪效果不佳。在此，我们提出BOBA（通过贝叶斯主动推断实现贝叶斯优化），这是一种受主动推断中自由能原理启发的新型采集函数，它显式地最小化动态环境中未来状态的预测不确定性。BOBA通过纳入前瞻性不确定性量化来扩展传统采集函数，该量化估计函数变化中的不确定性，从而在非平稳设置中实现更明智的探索-利用权衡。我们在合成动态基准上评估BOBA，并与最先进的DBO方法进行比较。我们的实验表明，BOBA在查询受限设置中显著改善了遗憾值，同时在时间受限设置中保持竞争力。我们进一步分析了具有不同探索策略的BOBA变体，展示了如何针对不同类型的动态函数调整探索-利用平衡。这项工作既为DBO贡献了基于自由能的采集函数，也提供了关于主动推断原理如何增强非平稳环境中优化的见解，对需要持续适应的实时应用具有意义。
英文摘要
Dynamic black-box optimization presents significant challenges for Bayesian Optimization (BO), as the objective function evolves over time, causing optimal locations to shift continuously. Existing dynamic BO (DBO) methods using standard acquisition functions such as Upper Confidence Bound (UCB) fail to explicitly account for temporal variations, leading to suboptimal sample allocation and poor tracking of moving optima. Here, we propose BOBA (Bayesian Optimization through Bayesian Active Inference), a novel acquisition function inspired by free energy principles from active inference that explicitly minimizes predictive uncertainty about future states in dynamic environments. BOBA extends traditional acquisition functions by incorporating a forward-looking uncertainty quantification that estimates uncertainty in function changes, enabling more informed exploration-exploitation trade-offs in non-stationary settings. We evaluate BOBA on synthetic dynamic benchmarks, comparing against state-of-the-art DBO methods. Our experiments demonstrate that BOBA significantly improves regret in query-restricted settings, while remaining competitive in time-limited settings. We further analyze variants of BOBA with different exploration strategies, showing how the exploration-exploitation balance can be tuned for different types of dynamic functions. This work contributes both a free energy-based acquisition function for DBO and insights into how active inference principles can enhance optimization in non-stationary environments, with implications for real-time applications requiring continuous adaptation.
