Mastery Checklist

Phase 1: Theoretical Foundations

• Can define nodes, edges, weights, and directed vs. undirected graphs.
• Understand the trade-offs between informed and uninformed search (BFS/DFS vs. Dijkstra/A*).
• Can explain why admissibility is required for optimal pathfinding.
• Can differentiate between Manhattan, Euclidean, Chebyshev, and Octile distance metrics.

Phase 2: Algorithmic Implementation

• Can manually trace an A* search iteration, including f, g, h cost calculation.
• Understand the importance of using Priority Queues (Min-Heaps) for the Open Set.
• Can implement a robust path reconstruction loop from goal to start.
• Can identify why a Hash Set is the standard data structure for the Closed Set.

Phase 3: Production Engineering & Optimization

• Can list at least three performance bottlenecks in naive A* implementations.
• Understand how Node Pooling and data locality improve performance.
• Can explain the advantages and specific use cases for Hierarchical A* (HPA*) and Jump Point Search (JPS).
• Understand the necessity of defensive programming and time-slicing for real-time applications.

Phase 4: Validation & Advanced Concepts

• Can design a multi-layered testing strategy (unit, integration, stress).
• Can explain the difference between admissible and consistent heuristics.
• Understand the principles behind dynamic pathfinding algorithms like D* Lite.
• Are aware of modern research areas like ML-enhanced heuristics and RL-based navigation.