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CS.462 Artificial Intelligence

CS.462 Artificial Intelligence. SOMCHAI THANGSATHITYANGKUL Lecture 03 : Search (2). Uniform Cost Search (UCS). Uniform Cost Search: Nodes in agenda keep track of total path length from start to that node Agenda kept in priority queue ordered by path length Get shortest path in queue.

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CS.462 Artificial Intelligence

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  1. CS.462Artificial Intelligence SOMCHAI THANGSATHITYANGKUL Lecture 03 : Search (2)

  2. Uniform Cost Search (UCS) • Uniform Cost Search: • Nodes in agenda keep track of total path length from start to that node • Agenda kept in priority queue ordered by path length • Get shortest path in queue

  3. Uniform Cost Search (UCS) Graph: 2

  4. UCS Agenda:

  5. O 151 F 71 S 99 Z 75 211 90 A 140 R B P 97 120 101 118 146 138 D 75 M T 111 70 L C Try this Find a path from node A to the goal node B. Use UCS method.

  6. O 151 F 71 S 99 Z 75 211 90 A 140 R B P 97 120 101 118 146 138 D 75 M T 111 70 L C UCS A Z75 T118 S140 T118 S140 O146 S140 O146L229

  7. Informed Search • If we use uniform cost search on this graph What’s wrong ?

  8. Informed Search • Informed Search or Heuristic Search methods try to estimate the “distance” to a goal state. A heuristic function h(s) is the estimated distance to the goal state. • Best-first greedy search - use h(s) instead of g(s) - Always expand the node with the least h(s) - Use a priority queue: Push in states with their second-half-cost h(s) Pop out the state with the least h(s) first.

  9. Best-first greedy search Graph:

  10. Best-first greedy search Agenda:

  11. Try this Use Best first greedy search path from Arad to Buchraest

  12. A* Search

  13. A* Search • Evaluation function f(n)= g(n)+ h(n) • g(n) the cost (from the start) to reach the node n. • h(n) estimated cost to get from the node n to the goal. • f(n) estimated total cost of path through n to goal.

  14. A* Search Graph:

  15. A* Search Agenda:

  16. Try this Use A* search path from Arad to Buchraest

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