Overview
What this challenge is about.
SAT-Based Planner for Smart-Grid Demand Response. Expert-level challenge in research. Conducting rigorous research on real questions, earn a blockchain-verif...
The Brief
What you'll do, and what you'll demonstrate.
Decide whether a SAT/MaxSAT planner beats the greedy heuristic on curtailment delivered within a 60-second dispatch budget.
This is not a research exercise. It is the work a researcher does to produce findings that withstand scrutiny. That distinction matters to every hiring manager who has seen candidates summarize papers and none who have produced original findings under expert review.
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
- Encode a real planning problem as SAT/MaxSAT
- Use an off-the-shelf solver under a strict time budget
- Compare against an industry baseline with realistic event data
- Reason about production integration of a SAT-based component
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Automated Planning
Master · Ai Systems
Strong alignment
This challenge maps to Automated Planning at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Sat Based Planning
Apply sat based planning to solve real industry problems and demonstrate production-level capability.
- Constraint Encoding
Apply constraint encoding 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.
- Experiment Design
Apply experiment design 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:
ML Researcher
Rigorous benchmarking of a SAT-based method against a production heuristic is exactly the experimental discipline ML researchers practice.
This challenge sharpens
- sat-based-planning
- benchmarking
- experiment-design
AI Engineer
SAT encoding on a real ops problem with a production-integration sketch is high-value AI engineering work in grid + supply-chain startups.
This challenge sharpens
- constraint-encoding
- domain-modeling
- sat-based-planning
Applied AI Scientist
Turning a solver experiment into a memo with revenue framing is what applied AI scientists do at infrastructure-AI companies.
This challenge sharpens
- benchmarking
- experiment-design
- domain-modeling