PYTHON
Free Section Starter Challenge
20 min
4. Heuristic Evaluation: Manhattan & Euclidean Metrics
Overview: Calculate domain-specific heuristic distances to guide search toward goal states.
Concept & Syntax Guide
A heuristic $h(n)$ estimates the cost of the cheapest path from node $n$ to a goal. For grid-based search where only orthogonal movements are permitted, the Manhattan Distance $h(n) = |x_1 - x_2| + |y_1 - y_2|$ is strictly admissible and never overestimates the true remaining cost.
Hands-On Practice Exercise
Calculate Manhattan Distance Heuristic
Compute the Manhattan distance between current state (1, 2) and goal state (4, 5).
Target Output:
Manhattan Heuristic Value: 6
Need a hint? Click here to reveal
Hint: Check syntax carefully and make sure your code contains:
Manhattan Heuristic Value: 6
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