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L28: A Star(A*) Search Algorithm in Artificial Intelligence with Examples | Informed Search in AI



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In this video you can learn about A Star(A*) Search in Artificial Intelligence with Solved Examples. It is an example of Informed Search in Artificial Intelligence. The video explains A Star(A*) Search Algorithm with advantages and disadvantages. This topic is very important for College University Semester Exams and Other Competitive exams.

A* Search Algorithm in Artificial Intelligence with Solved Examples

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39 thoughts on “L28: A Star(A*) Search Algorithm in Artificial Intelligence with Examples | Informed Search in AI
  1. Thank you sir for providing quality education.
    I am Muslim and my religion Islam teaches us to invite others to this true religion of Almighty God.
    I invite everyone to learn and understand the Islam.I invite you all to read the translation of the religious book of Islam "Al-Quran"

  2. I think you dont need to explore further if a goal node path is less than all the previously left out paths
    (In example 2, 6 is less than 10,7,11 so s -> a -> c -> g will be the final path)

  3. Yaar aap kahan thay aab tak.Maa 2nd semester maa hoo BsAI kay mujhay itni achi samajh pehlay kabhi nahi aai AI ky algorithms ki.

  4. I want to know how did you get heuristics value here you may have taken randomly but in code how should we take in similar lroblem eucledian distance is not possible I guess

  5. Sir you explained it beautifully, but I just want to know that how to find heuristic value because in exam how do we figure out the heuristic value because it would be difficult to cram the exact heuristic value.

  6. sir way are you confuse us if we check the lowest f(n) value every time to take the next step then we find the smallest path according to this algorithm so why we check the other path which you check here like in second example we know that we find the smallest f(n) of path s to a to c to g then why you check other they give the high value from this

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