A* Searching Algorithm-Artificial Intelligence-15A05606-Unit-1-Problem Solving-Informed Searching

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  • เผยแพร่เมื่อ 16 พ.ค. 2021
  • Unit - 1 - Problem Solving
    Informed Searching Strategies -A* Search
    Greedy Best First Search minimizes a heuristic h(n) which is an estimated cost from a current state n to the goal state.
    Greedy Best First Search is efficient but it is not optimal and not complete.
    Uniform Cost Search minimizes the cost g(n) from the initial state to current state n.
    Uniform Cost Search is optimal and complete but not efficient.
    A* Search: Combine Greedy Best First Search and Uniform Cost Search to get an efficient algorithm which is complete and optimal.
    A* search evaluates nodes by combining g(n), the cost to reach the node and h(n), the cost to get from the node to the goal.
    f(n) = g(n) + h(n)
    f(n) is the evaluation function which gives the cheapest solution cost
    g(n) is the exact cost to reach node n from the initial state.
    h(n) is an estimation of the assumed cost from current state (n) to reach the goal.
    A* with f() not Admissible when h() overestimates the cost to reach the goal state
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ความคิดเห็น • 22

  • @prasanthkumar6393
    @prasanthkumar6393 ปีที่แล้ว +4

    Your videos are very helpful mam
    Day after tomorrow we have ai exam
    Your channel is life saviour for ai subject.

  • @heybuddy_JAI_SHREE_RAM
    @heybuddy_JAI_SHREE_RAM 6 หลายเดือนก่อน +3

    Best videos for AI

    • @WinningCSE
      @WinningCSE  6 หลายเดือนก่อน +2

      Thank you

  • @asikhossain7191
    @asikhossain7191 2 ปีที่แล้ว +1

    this realy helpfull

    • @WinningCSE
      @WinningCSE  2 ปีที่แล้ว +1

      Thank you Asik Hossain

  • @udaykiranrh
    @udaykiranrh 5 หลายเดือนก่อน +1

    Mam for in greedy best first search you took total of h(n) values as path cost ,in this you took original path cost,is it like that only??

    • @WinningCSE
      @WinningCSE  5 หลายเดือนก่อน +2

      To search the goal state, initially we take some assumed cost h(n), I.e. cost from current state to goal state, after exploring we calculated the original cost g(n), I.e. initial state to current state.
      Hence h(n) is not original path cost but used to find next state to explore.

    • @udaykiranrh
      @udaykiranrh 5 หลายเดือนก่อน +1

      @@WinningCSE Thank you Mam.

    • @udaykiranrh
      @udaykiranrh 5 หลายเดือนก่อน

      @@WinningCSEMam should we take Sum of Heuristic values for Greedy Best First search problems?

    • @WinningCSE
      @WinningCSE  5 หลายเดือนก่อน

      @@udaykiranrh welcome

  • @aaronelectronik4762
    @aaronelectronik4762 ปีที่แล้ว +1

    Thank you for this great video.
    But could you explain aigin how do you find the red values between different nodes?? for example 75 between A and B, 140 between A and E und 118 between A and C
    Thank you in advance

    • @WinningCSE
      @WinningCSE  ปีที่แล้ว +1

      Great suggestion!

    • @aaronelectronik4762
      @aaronelectronik4762 ปีที่แล้ว

      @@WinningCSE may you explain it here in the commentar please??

  • @user-xq2kn4ts3k
    @user-xq2kn4ts3k 4 หลายเดือนก่อน +1

    🫶👍

    • @WinningCSE
      @WinningCSE  4 หลายเดือนก่อน +1

      Thank you

  • @mkalyan2698
    @mkalyan2698 2 ปีที่แล้ว +1

    good explanation madam thank you madam. gv me u r mail id madam

    • @WinningCSE
      @WinningCSE  2 ปีที่แล้ว +1

      Thank you Mr. Kalyan. Are you a staff or student

    • @mkalyan2698
      @mkalyan2698 2 ปีที่แล้ว

      @@WinningCSE Mtech student madam from NIT silchar

    • @WinningCSE
      @WinningCSE  2 ปีที่แล้ว

      @@mkalyan2698 that's great. My mailid is sumathi@kec.ac.in

  • @hemajoshi7438
    @hemajoshi7438 5 หลายเดือนก่อน +1

    Your videos are very helpful for making notes thanks a tons mam ❤🫶

    • @WinningCSE
      @WinningCSE  4 หลายเดือนก่อน +2

      Thanks for your comments, keep watching, all the best.