Overview
What this challenge is about.
Plan Warehouse Pick Routes with a Classical Planner. Advanced challenge in code. Writing production code that solves real engineering problems, earn a blockc...
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.
This is not a coding exercise. It is the work a software engineer does between a Jira ticket and a merged PR. That distinction matters to every hiring manager who has seen candidates solve LeetCode problems and none who have shipped production code under real constraints.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
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