Collaborative Bayesian Optimization Leveraging Human Expertise for Engineering System Design

المفاهيم الأساسية

Bayesian optimization can efficiently optimize expensive-to-evaluate functions, but often overlooks valuable insights from domain experts. This work presents a collaborative approach that enables experts to influence the selection of experiments, improving convergence, accountability, and interpretability.

الملخص

The content discusses a methodology for human-in-the-loop Bayesian optimization that enables continuous expert input while remaining practical and easy to use. Key points:

• The approach exploits the hypothesis that humans are more efficient at making discrete choices rather than continuous ones. It applies high-throughput (batch) Bayesian optimization alongside discrete decision theory to enable domain experts to influence the selection of optimal experiments.

• At each iteration, a multi-objective optimization problem is solved to obtain a set of alternative solutions that have both high utility and are reasonably distinct. The expert then selects the desired solution for evaluation from this set, allowing for the inclusion of expert knowledge.

• The methodology is benchmarked on numerical and applied case studies, including bioprocess optimization and reactor geometry design. It demonstrates improved performance across a range of expert abilities compared to standard Bayesian optimization.

• The approach enables improved expert knowledge integration, accountability, and interpretability in decision-making, contributing to the field of human-AI collaboration.

الإحصائيات

Bayesian optimization can efficiently optimize expensive-to-evaluate functions, but often overlooks valuable insights from domain experts.
The proposed approach exploits the hypothesis that humans are more efficient at making discrete choices rather than continuous ones.
High-throughput (batch) Bayesian optimization is used alongside discrete decision theory to enable domain experts to influence the selection of optimal experiments.
The approach is benchmarked on numerical and applied case studies, demonstrating improved performance across a range of expert abilities compared to standard Bayesian optimization.

اقتباسات

"By removing the human from decision-making processes in favor of maximizing statistical quantities such as expected improvement, complex functions can be optimized efficiently."
"Our methodology exploits the hypothesis that humans are more efficient at making discrete choices rather than continuous ones and enables experts to influence critical early decisions."
"Through the inclusion of continuous expert opinion, our approach enables faster convergence, and improved accountability for Bayesian optimization in engineering systems."

الرؤى الأساسية المستخلصة من

Human-Algorithm Collaborative Bayesian Optimization for Engineering Systems

by Tom Savage,E... في arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.10949.pdf

استفسارات أعمق

How can the proposed approach be extended to handle cases where the expert's knowledge is uncertain or inconsistent?

In cases where the expert's knowledge is uncertain or inconsistent, the proposed approach can be extended by incorporating probabilistic models or fuzzy logic techniques to represent the varying degrees of certainty or inconsistency in the expert's knowledge. This can be achieved by assigning probabilities or confidence levels to the expert's input or by allowing for fuzzy sets to capture the ambiguity in the expert's reasoning.
Additionally, the approach can be enhanced by introducing a feedback mechanism that iteratively refines the expert's input based on the outcomes of previous decisions. This feedback loop can help calibrate the expert's knowledge over time and adapt to changing circumstances or new information.
Furthermore, the inclusion of multiple experts with diverse perspectives can help mitigate the impact of uncertain or inconsistent knowledge. By aggregating inputs from different experts, the approach can leverage the collective wisdom and diversity of opinions to make more robust and informed decisions.

What are the potential limitations of the discrete decision-making approach, and how could it be further improved to better capture the expert's reasoning process?

One potential limitation of the discrete decision-making approach is the restriction it imposes on the expert's ability to express nuanced preferences or trade-offs between solutions. To address this limitation, the approach could be enhanced by allowing for more granular decision-making, such as incorporating continuous sliders or weighting mechanisms that enable the expert to express degrees of preference or uncertainty.
Another limitation is the potential for decision fatigue or cognitive overload, especially when presented with a large number of alternative solutions. To mitigate this, the approach could implement intelligent algorithms that prioritize and present the most relevant or diverse set of alternatives to the expert, reducing the cognitive burden and facilitating more effective decision-making.
Furthermore, the approach could benefit from incorporating explainable AI techniques that provide transparency into the decision-making process. By offering insights into how the algorithm arrived at certain recommendations or solutions, the expert can better understand and trust the system, leading to more effective collaboration.

How could the integration of human expertise in Bayesian optimization be leveraged to drive innovation and discovery in other domains beyond engineering, such as scientific research or medical decision-making?

The integration of human expertise in Bayesian optimization can drive innovation and discovery in various domains beyond engineering by harnessing the domain knowledge and intuition of experts to guide the optimization process. In scientific research, experts can provide valuable insights into experimental design, hypothesis generation, and data interpretation, leading to more targeted and impactful research outcomes.
In medical decision-making, the integration of human expertise can enhance patient care by personalizing treatment plans, optimizing clinical workflows, and improving diagnostic accuracy. Experts in healthcare can contribute their clinical experience and domain-specific knowledge to optimize treatment strategies, identify novel biomarkers, and enhance patient outcomes.
Moreover, the collaborative approach can foster interdisciplinary collaboration and cross-pollination of ideas, leading to novel solutions and breakthroughs at the intersection of different fields. By bringing together experts from diverse backgrounds, the integration of human expertise in Bayesian optimization can drive innovation, discovery, and transformative change across a wide range of domains.