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WeAreDevelopers LIVE • Nov 17, 2020

Augmented Intelligence for transport planning: Human in the Loop Modelling

Stefan Petrov

Why do human operators reject mathematically perfect transport plans? Discover how probabilistic programming and human-in-the-loop frameworks bridge the gap between rigid optimization and real-world execution.

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#1 about 4 min

Introducing human-in-the-loop decision support for logistics planning

Distributing complex automated processes to local planners requires systems that handle incomplete data alongside human oversight.

#2 about 5 min

Decoupling problem representation and solution using mathematical optimization

Framing business rules as explicit mathematical structures allows programmatic solvers to handle massive configurations efficiently.

#3 about 6 min

Navigating the mismatch between declarative optimization and imperative workflows

Reconciling global algorithm objectives with localized human decision-making prevents rigid automated plans from completely failing during deployment.

#4 about 5 min

Evaluating confounding factors and bias in machine learning models

Identifying underlying data collection errors ensures operators trust algorithmic predictions beyond surface-level accuracy scores.

#5 about 6 min

Integrating algorithmic decision support into local operational planning workflows

Bridging automated operations with partial plan execution capabilities builds trust and adapts to unrecorded logistical conditions.

#6 about 7 min

Handling missing package parameters using probabilistic programming and sampling

Generating Bayesian estimates for unrecorded inputs enables transportation systems to accurately project total truck capacities.

#7 about 3 min

Extracting interpretable operational rules from complex tree-based models

Converting vast random forest structures into shallow logic statements allows domain experts to validate and correct underlying predictive logic.

#8 about 2 min

Displaying predictive statistical uncertainty to empower local decision makers

Exposing statistical confidence intervals helps human operators determine when to override software outputs using real-time situational context.

#9 about 9 min

Evaluating hypothetical business scenarios via interactive linear constraint editing

Allowing users to toggle strict operational limiters enables realistic comparisons between idealized optimization pathways and baseline resource costs.

#10 about 3 min

Validating model assumptions with active learning and expert feedback

Deploying intelligent sampling interfaces captures targeted domain knowledge to systematically refine mathematical boundaries.

Matching moments

15:08 min

Audience questions on practical machine learning operational strategies

Lina Weichbrodt · LIVE

6:34 min

Commercial applications of modern artificial intelligence and machine learning

Kris Howard · LIVE

2:03 min

Solving complex engineering challenges in artificial intelligence deployment

Nico Axtmann · WWC 2022

3:20 min

Scaling intelligent decision making across software automation workflows

Cassie Kozyrkov · WWC 2022

1:30 min

Transitioning from machine learning models to complex agentic systems

Alejandro Saucedo Alejandro Saucedo · WWC 2025

4:55 min

Audience Q&A on autonomous driving models and data

Liang Yu · WWC 2022

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