Path Finding using Search Algorithms

A33de8445891945c81e488c9ce71e8cc?s=47 Mann
May 11, 2015

Path Finding using Search Algorithms

A33de8445891945c81e488c9ce71e8cc?s=128

Mann

May 11, 2015
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Transcript

  1. Path Finding Problem Zolbayar Magsar Chanmann Lim Yihan Xu

  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!
  4. Problems to be studied • Search Algorithms • Visualization

  5. Existing Work Implemented in NodeJS (Raphaël)

  6. Our Implementation We implemented in Java (Swing) adding local searches

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

    First Search • Greedy Best First Search • A* Search • Hill Climbing Search • Simulated Annealing
  8. Algorithm - Uninformed Search Breadth First Search Depth First Search

  9. Best First Search A* Search Algorithm - Heuristic Search

  10. Algorithm - Local 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.
  16. Q&A Thank You