PYTHON Free Section Starter Challenge 20 min

1. State Space Search & Production Systems

Overview: Formulate problems as states, initial states, goal states, and valid transition operators.

Concept & Syntax Guide

An AI problem is formally defined by four components: (1) Initial State $S_0$, (2) Set of Actions or Operators mapping State to State, (3) Goal Test determining if the current state satisfies objectives, and (4) Path Cost function. In this lesson, we represent grid navigation as a state transition system in Python.

Hands-On Practice Exercise

Generate Valid State Transitions

Given a 2D coordinate grid (x, y), write a function `get_neighbors(x, y)` that returns all 4 valid cardinal moves (Up, Down, Left, Right) within bounds 0 to 4. Count and print total valid moves from (2, 2).

Target Output: Valid next states: 4 possible moves generated

Need a hint? Click here to reveal

Hint: Check syntax carefully and make sure your code contains: Valid next states: 4 possible moves generated

PYTHON Code Editor

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# State Space Search: Cardinal Transitions
def get_neighbors(x, y, max_val=4):
    moves = [(0, 1), (0, -1), (1, 0), (-1, 0)]
    valid = []
    # Loop over moves and check bounds 0 <= nx <= max_val and 0 <= ny <= max_val
    for dx, dy in moves:
        nx, ny = x + dx, y + dy
        if 0 <= nx <= max_val and 0 <= ny <= max_val:
            valid.append((nx, ny))
    return valid

# Test transitions from coordinate (2, 2)
transitions = get_neighbors(2, 2)
print(f"Valid next states: {len(transitions)} possible moves generated")