Project 5: The Sudoku Solver (Constraint Satisfaction + Backtracking)

Build a Sudoku solver using constraint satisfaction and backtracking search.

Quick Reference

Attribute Value
Difficulty Level 3: Advanced
Time Estimate 10-16 hours
Language Python
Prerequisites Recursion, basic algorithms
Key Topics CSP, backtracking, heuristics

1. Learning Objectives

By completing this project, you will:

  1. Model Sudoku as a CSP.
  2. Implement backtracking with constraint checks.
  3. Add heuristics like MRV and forward checking.
  4. Measure solver performance.
  5. Visualize solving steps.

2. Theoretical Foundation

2.1 CSPs

Constraint satisfaction problems search for assignments that satisfy constraints. Sudoku is a classic CSP.

3. Project Specification

3.1 What You Will Build

A Sudoku solver that takes a puzzle and outputs a solution with traceable steps.

3.2 Functional Requirements

  1. Board representation with constraints.
  2. Backtracking solver with validity checks.
  3. Heuristics for variable selection.
  4. Performance metrics (nodes visited, time).
  5. Solution trace (optional).

3.3 Non-Functional Requirements

  • Deterministic solutions.
  • Readable output grid.
  • Configurable heuristics.

4. Solution Architecture

4.1 Components

Component Responsibility
CSP Model Represent constraints
Solver Backtracking search
Heuristics Improve search order
Reporter Metrics and traces

5. Implementation Guide

5.1 Project Structure

SYMBOLIC_AI_AND_EXPERT_SYSTEMS_MASTERY/P05-sudoku/
├── src/
│   ├── board.py
│   ├── solver.py
│   ├── heuristics.py
│   └── report.py

5.2 Implementation Phases

Phase 1: CSP model (3-4h)

  • Define constraints for rows/cols/boxes.
  • Checkpoint: constraint checks work.

Phase 2: Backtracking solver (4-6h)

  • Implement search and backtracking.
  • Checkpoint: solver completes easy puzzles.

Phase 3: Heuristics + metrics (3-6h)

  • Add MRV and forward checking.
  • Checkpoint: performance improves.

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit constraints valid placements
Integration solver solves known puzzles
Regression heuristics stable node counts

6.2 Critical Test Cases

  1. Solver handles easy and hard puzzles.
  2. Heuristics reduce nodes visited.
  3. Solution validates all constraints.

7. Common Pitfalls & Debugging

Pitfall Symptom Fix
Slow solver long runtime add MRV/forward checking
Wrong solution invalid grid validate constraints every step
Non-termination infinite loop enforce recursion limits

8. Extensions & Challenges

Beginner

  • Add puzzle generator.
  • Add CLI for input.

Intermediate

  • Add constraint propagation (AC-3).
  • Add visualization of solving steps.

Advanced

  • Add SAT solver comparison.
  • Add performance profiling.

9. Real-World Connections

  • Scheduling problems use CSPs and backtracking.
  • AI planning relies on similar search strategies.

10. Resources

  • CSP textbooks
  • Sudoku solver references

11. Self-Assessment Checklist

  • I can model Sudoku as a CSP.
  • I can implement backtracking with heuristics.
  • I can measure solver performance.

12. Submission / Completion Criteria

Minimum Completion:

  • Sudoku solver with backtracking

Full Completion:

  • Heuristics + performance metrics

Excellence:

  • AC-3 integration
  • Puzzle generator

This guide was generated from project_based_ideas/AI_AGENTS_LLM_RAG/SYMBOLIC_AI_AND_EXPERT_SYSTEMS_MASTERY.md.