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Bayesian Search & Rescue simulation: use Bayes’ rule + noisy search-effectiveness to update target probabilities and find a lost sailor on an OpenCV map. Includes Monte Carlo strategy tester & planned vs. actual SEP

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Bayes SAR (Search & Rescue Game)

A small Python game that uses Bayes’ rule to guide a Coast Guard–style search across three areas. You’ll see a map window (via OpenCV) and a text menu in your terminal.


Requirements

  • Python 3.9+ (works well with Anaconda)
  • Libraries: NumPy, OpenCV
# pip
pip install numpy opencv-python

# or conda
conda install numpy opencv

How to Play

  1. Start the game (run your script, e.g. python main.py).
  2. Check the planned search effectiveness values: E1_plan, E2_plan, E3_plan.
  3. Type a menu number 1–9 to choose where (and how) to search.
  4. Keep searching until you find the sailor — a blue dot will appear on the map.

Controls

  • 1–9 → choose search options/areas
  • 0 → quit
  • 7 → restart
  • 8 → test strategies
  • 9 → show coverage

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Bayesian Search & Rescue simulation: use Bayes’ rule + noisy search-effectiveness to update target probabilities and find a lost sailor on an OpenCV map. Includes Monte Carlo strategy tester & planned vs. actual SEP

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