Breadth First Search - Part 1

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  • เผยแพร่เมื่อ 2 ก.พ. 2018
  • The simplest version of breadth-first search. This version doesn't use a visited set but still finds the shortest path from the start state to a goal state. Part 2 will show you how to use a visited set to potentially make the search more efficient.

ความคิดเห็น • 28

  • @Jajdjejwi28
    @Jajdjejwi28 3 ปีที่แล้ว +36

    I wasted hours at my uni with professors that overcomplicated these search algorithms. You sir saved me big time!

  • @mariagabrielagarciaarroyo4733
    @mariagabrielagarciaarroyo4733 6 ปีที่แล้ว +15

    Loving this channel, super well explained. Thank you!

  • @raphaellmsousa
    @raphaellmsousa 6 ปีที่แล้ว +2

    Congratulations for your explanation! It was the best one for this topic for me!

  • @nerioamaral4045
    @nerioamaral4045 3 ปีที่แล้ว +6

    I love John's Lecture so helpful when it comes to explaining, I would love to have him as my AI Professor. anyways Thanks Dr John

  • @abhilamsal7185
    @abhilamsal7185 3 ปีที่แล้ว +5

    Breadth First Search applies goal test to each node when it is generated rather than when the node is selected for expansion. Therefore, once the algorithm determines shallowest goal node, it stops the search.
    By following your explaination, the time complexity of BFS comes around in the order of O( b^ (n+1) ).
    However, if the nodes were to be tested for goal nodes when they were generated rather than when selected for expansion, time complexity becomes O(b ^ n) since whole layer of nodes at depth n would be expanded before goal was detected.
    b -- branching factor
    n -- depth of shallowest goal node.

  • @benaya6
    @benaya6 4 ปีที่แล้ว

    the best explanation I found so far...thank you sir

  • @yourdoom9868
    @yourdoom9868 6 ปีที่แล้ว +1

    Thanks for the explanation sir!

  • @syntaxerror2144
    @syntaxerror2144 3 หลายเดือนก่อน

    Amazing work brother!

  • @kelvinmonari5512
    @kelvinmonari5512 18 วันที่ผ่านมา

    You are the best profesor ever

  • @gabethebabe3840
    @gabethebabe3840 5 ปีที่แล้ว

    WOW great video!

  • @abhirustagi6969
    @abhirustagi6969 5 ปีที่แล้ว

    thankyou sir!!! very well explained

  • @mustafaalshaqaq2303
    @mustafaalshaqaq2303 2 ปีที่แล้ว

    Thanks. This is very helpful.

  • @limitless1692
    @limitless1692 4 ปีที่แล้ว +4

    Wow simple and clear
    Thank you Sir
    Much appreciated :)

  • @ruixincheng6201
    @ruixincheng6201 4 ปีที่แล้ว

    great explanation!!! could you please upload more videos about Neural networks and probability problems? such as Bayes networks,approximate inference and CNN

  • @franchello1105
    @franchello1105 6 ปีที่แล้ว

    A couple of drawbacks to bring up. Not guaranteed to find the least costed path from S to a Goal State. Also would use a ton of storage to maintain all the current unexpanded nodes. Back in college we had to solve the canibal missionary problem using DFS, because the storage required would grow exponentially if BFS is used.

  • @user-ov1rt6pi7d
    @user-ov1rt6pi7d 3 ปีที่แล้ว

    thank you it was great ❤🌹

  • @yourfellowmuslimyagi2065
    @yourfellowmuslimyagi2065 2 ปีที่แล้ว

    Excellent explanation

  • @fidelhernandez2322
    @fidelhernandez2322 5 ปีที่แล้ว

    If let's say there is only one goal state (G1). Could I accept G1 as the goal state as soon as I add it to the frontier? By the way thank you for your clear explanation!

  • @jerushanmoodley2641
    @jerushanmoodley2641 4 ปีที่แล้ว

    Well explained

  • @tzaswit
    @tzaswit 3 ปีที่แล้ว

    Thank you

  • @nadaali5
    @nadaali5 5 ปีที่แล้ว

    thank u

  • @sknganga3633
    @sknganga3633 2 ปีที่แล้ว

    This guy saving my degree

  • @amirhoxha6231
    @amirhoxha6231 11 หลายเดือนก่อน

    smart fella

  • @hashithakularatne3646
    @hashithakularatne3646 ปีที่แล้ว

    don't we need to add all the visited letters for that path?

  • @kjohari_
    @kjohari_ 3 ปีที่แล้ว

    Missing your videos sir

  • @mr.olorinthemaia
    @mr.olorinthemaia 10 หลายเดือนก่อน

    radu cretulescu trece-ma la examen