CS6850 Foundations of AI Assignment Help | Wright State University
- R K Gaur
- Sep 15, 2025
- 3 min read
Updated: Oct 4, 2025
Are you enrolled in CS6850 - Foundations of AI at Wright State University and struggling with the A-Star () algorithm implementation for the RushHour Game? Don’t worry, you are in the right place. This graduate-level course is demanding, but now you don’t have to handle the complexity alone.
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Sample Assignment Question from CS6850
Assignment 2 – Foundations of AI
Task: Implement the A-Star () algorithm to solve the RushHour Game.
Write a function A-Star() in Python that returns a sequence of moves to reach the solution.
Use heuristics (custom-designed, since h1 and h2 may not apply).
Don’t modify the base RushHour code.
Provide your own test cases in addition to the given ones.
Properly comment your code.
Objective of the Game: Guide the red car out of the gridlock by moving other vehicles that block its path. Cars and trucks can only move in straight lines, creating a layered problem of obstacles.
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We provide complete assistance with:
A-Star Algorithm Implementation
- Efficient node expansion and heuristic-based pathfinding.
Custom Heuristics Design
- Beyond standard h1/h2 – tailored for RushHour.
Test Case Creation
- Covering simple to complex board configurations.
Clean & Commented Code
- Well-documented Python code that professors love.
Error-Free Execution
- Guaranteed working solutions tested on multiple difficulty levels.
What You Learn in CS6850
This course focuses on the fundamental principles of Artificial Intelligence, including:
Search algorithms (DFS, BFS, UCS, A*)
Heuristic design and evaluation
Problem-solving strategies in constrained environments
Python implementation of AI models
Application of AI in puzzle-solving and optimization tasks
Assignments are designed to build practical AI problem-solving skills that simulate real-world AI applications.
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Understanding the A-Star Algorithm
The A-Star algorithm is a popular pathfinding and graph traversal method. It is widely used in AI for games and robotics. The algorithm finds the shortest path from a start node to a target node. It uses a heuristic to estimate the cost from the current node to the target. This helps in efficiently guiding the search process.
How A-Star Works
A-Star combines features of Dijkstra’s algorithm and Greedy Best-First Search. It maintains a priority queue of nodes to explore. Each node has a cost value, which is the sum of two components:
g(n): The cost from the start node to the current node.
h(n): The estimated cost from the current node to the target node.
The algorithm selects the node with the lowest total cost (f(n) = g(n) + h(n)) for exploration. This balance allows A-Star to find the most efficient path while considering both the cost incurred and the estimated future cost.
Implementing A-Star in Python
To implement the A-Star algorithm in Python, you will need to:
Define the graph structure for the RushHour game.
Create a priority queue to manage the nodes.
Implement the A-Star function that calculates the path.
Here’s a simple outline of how the function might look:
```python
def A_Star(start, goal):
open_set = PriorityQueue()
open_set.put(start, 0)
came_from = {}
g_score = {start: 0}
f_score = {start: heuristic(start, goal)}
while not open_set.empty():
current = open_set.get()
if current == goal:
return reconstruct_path(came_from, current)
for neighbor in get_neighbors(current):
tentative_g_score = g_score[current] + cost(current, neighbor)
if tentative_g_score < g_score.get(neighbor, float('inf')):
came_from[neighbor] = current
g_score[neighbor] = tentative_g_score
f_score[neighbor] = g_score[neighbor] + heuristic(neighbor, goal)
if neighbor not in open_set:
open_set.put(neighbor, f_score[neighbor])
return False # Path not found
```
Custom Heuristics for RushHour
In the context of the RushHour game, you may need to design custom heuristics. Standard heuristics like Manhattan distance might not suffice due to the unique constraints of the game. Consider factors like the number of blocked paths or the distance to the exit for more effective heuristics.
Creating Test Cases
Testing your implementation is crucial. You should create various test cases that cover:
Simple scenarios with few cars.
Complex scenarios with multiple obstacles.
Edge cases where the solution is not straightforward.
Conclusion
The A-Star algorithm is a powerful tool for solving pathfinding problems. By mastering its implementation, you will enhance your understanding of AI principles. Remember, we are here to support you through your CS6850 journey. Don’t hesitate to reach out for assistance with your assignments.
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