Appendix C — Experiment Code

This directory provides minimal, runnable Python examples for Appendix C: Course Labs and Practice Projects.

Design principles:

• Prefer the Python standard library only, to lower the setup barrier

• Each lab runs standalone

• Emphasize methodology and engineering skeletons—not big data or peak performance

• Easy to swap in real SkyNetUamPlatform components, graph stores, model services, and LLM APIs

Labs

Lab	Topic	Related Chapters
Lab 1	Small low-altitude ontology and rule encoding	Ch. 5–6
Lab 2	Static risk reasoning with KG + SPARQL-style queries	Ch. 8–10
Lab 3	Conflict-detection train/eval on temporal snapshot features	Ch. 11–12
Lab 4	Prediction sets and empirical coverage for risk outputs	Ch. 15
Lab 5	Cloud–edge collaboration and event-driven prototype	Ch. 18–20
Lab 6	Neuro-symbolic agent: KG retrieval + tool calls + rule-bounded planning	Ch. 22

How to run

python lab1_ontology_rules.py
python lab2_static_risk_reasoning.py
python lab3_tkg_conflict_detection.py
python lab4_conformal_prediction.py
python lab5_edge_cloud_pipeline.py
python lab6_ns_agent.py

Suggested order

1. Run Lab 1 and Lab 2 for the knowledge substrate and static reasoning

2. Run Lab 3 and Lab 4 for dynamic risk and trustworthy calibration

3. Run Lab 5 and Lab 6 for deployment patterns and agent loops