Overview
What this challenge is about.
Write a PDDL domain and problem generator for warehouse pick routes, compare two planners on cost and congestion, and get a verifiable certificate.
The scenario
The startup (around 90 staff) runs about 200 robots across 12 EU warehouses; even a 5 percent reduction in pick-minutes is worth around EUR 400k annually in robot-hour avoidance.
The Brief
What you'll do, and what you'll demonstrate.
Decide whether a classical PDDL planner beats the hand-coded pick-route heuristic on plan cost and solve-time under realistic shift loads.
Earning criteria — what you'll demonstrate
- Model a real operational problem in PDDL
- Apply state-space search and heuristic-guided planning in practice
- Benchmark planner trade-offs (cost vs. time) honestly
- Translate planning results into a written engineering recommendation
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Pddl Modeling
Apply pddl modeling to solve real industry problems and demonstrate production-level capability.
- State Space Search
Apply state space search to solve real industry problems and demonstrate production-level capability.
- Classical Planning
Apply classical planning to solve real industry problems and demonstrate production-level capability.
- Benchmarking
Apply benchmarking to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Domain Modeling
Apply domain modeling to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
AI Engineer
Modeling an operational problem in PDDL and benchmarking planners is exactly the AI engineering work at any robotics or scheduling-AI company.
This challenge sharpens
- pddl-modeling
- classical-planning
- domain-modeling
Applied AI Scientist
Comparing search-based methods on a real operational benchmark and writing the recommendation memo is core applied AI scientist work.
This challenge sharpens
- state-space-search
- benchmarking
- pddl-modeling
ML Researcher
Treating planner choice as a rigorous experiment is the methodological discipline ML researchers bring to symbolic AI projects.
This challenge sharpens
- state-space-search
- benchmarking
- classical-planning