Mann
May 11, 2015

# Path Finding using Search Algorithms

May 11, 2015

## Transcript

2. ### Introduction • Solve Path Finding problem • Find the routing

path on a grid from a given square A to destination B using: - Uninformed search algorithms (Breadth First, Depth First) - Heuristic search algorithms ( Best First, A*) - Local Search algorithms (Hill-climbing, Simulated annealing) • Make use of Manhattan distance heuristics for the informed search and scoring function for local search.
3. ### Motivation Seeing search in action so AI students can understand

search better!

7. ### Detailed Aspects Algorithm Used • Breadth First Search • Depth

First Search • Greedy Best First Search • A* Search • Hill Climbing Search • Simulated Annealing

11. ### Cooling schedule • Initial Temperature : 1000 • Temperature decrease

function: Linear Multiplicative Cooling
12. ### Detailed Aspects Running Performance Execution Time Comparison 0 1 2

3 4 5 Sample 1 2 3 4 5 6 7 8 9 10 Breadth First Depth First Best First A* Hill-Climbing Simulated Annealing
13. ### Detailed Aspects Running Performance Path Cost Comparison 0 100 200

300 400 500 600 700 Sample 1 2 3 4 5 6 7 8 9 10 Breadth First Depth First Best First A* Hill-Climbing Simulated Annealing
14. ### Detailed Aspects Running Performance Expanded Nodes Comparison 0 100 200

300 400 500 600 Sample 1 2 3 4 5 6 7 8 9 10 Breadth First Depth First Best First A* Hill-Climbing Simulated Annealing
15. ### Conclusion • Breadth First Search is guaranteed to ﬁnd the

goal, the Depth First can reach the goal only in ﬁnite state space. • Greedy Best First Search ﬁnds suboptimal path, and A* Search is guaranteed to ﬁnd the shortest path if the heuristic is never larger than the true distance. In the implementation, we use Manhattan Heuristic. • Hill-Climbing Search and Simulated Annealing are not even guaranteed to ﬁnd a solution, have linear time/space advantage. • A* is a good choice for most pathﬁnding needs.