Lecture 3: Informed Search (A*)

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  • Опубліковано 5 лис 2024

КОМЕНТАРІ • 11

  • @tedchirvasiu
    @tedchirvasiu 10 років тому +5

    Thank you so much for posting these, extremely useful.

  • @elliottwen3712
    @elliottwen3712 10 років тому +3

    I am wondering why f(n) is less than or equal f(G).

    • @scott4033
      @scott4033 10 років тому +1

      Hey, just watched this and I was a little bit confused at first also. I think his explanation complicates it a little bit, because really this is being assumed by definition before the thing even begins. When he says that the heuristic is "optimistic" -- then it means that BY DEFINITION it is underestimating the cost of reaching the goal. So if it is underestimating, then it MUST be less than the actual cost. So f(n) < f(G) .
      But then that's a big assumption, and the thing breaks if it isn't true. So that's why I think he makes a point to emphasize that it is important to get the heuristic estimations correct before hand, and make certain that it is actually underestimating the cost of the path to the goal.

    • @bensonmiakoun7674
      @bensonmiakoun7674 4 роки тому +1

      @@scott4033 thanks!

  • @ColleeneMcCurdy
    @ColleeneMcCurdy 2 роки тому

    thank you

  • @WahranRai
    @WahranRai 5 років тому +1

    What happen in Manhattan (repeat it 10 times rapidly) !!!

  • @denniskim2352
    @denniskim2352 5 років тому

    are these slides available online

  • @shebotnov
    @shebotnov 10 років тому +1

    what semester is this being taught in? I mean it is a lecture about AI yet students dont know what a BFS or DFS is. Yet this stuff you are taught in Algorithms and Data structures, which is normally at the start of the CS course. In any case AI should be taught after AD for obvious reasons (to not waste time with such trivial topics)

  • @tedchirvasiu
    @tedchirvasiu 10 років тому

    Lol, Romaniaaa!

    • @shebotnov
      @shebotnov 10 років тому +2

      it is a standard example for graph search and this slide is used around the whole world.