Thanks for the video! Even though this is a relatively simple algorithm, the only way to really understand it is to go step by step on a toy problem, like you did. Excellent job!
While examining the children of a maximizer, if v of maximizer > beta, prune the rest of the children. While examining the children of a minimizer, if v of minimizer < alpha, prune the rest of the children.
Some important notes: 1) only in a Max node can update the corresponding alpha, so does Min for beta. 2) v can only be returned up to its parent 3) alpha and beta can only be passed down from its parent 4) cut the current node from the tree whenever alpha >= beta
+Dinh Hung Nguyen Attention please at minute 2:28 !!! Hello :) Thank you very much for your explaination. It is very helpful. The algorithm checks whether the value 4 is higher or equal !!! to beta. Thank you, BR
A very good explanation. A lot of people are complaining about how this could be more simple, but look at the title, it shows you if how exactly algorithm would do this.
Very well detailed explanation of alpha beta pruning given Sir! You are really a good teacher, saved me from this confusing algo! I was tearing my hair getting to understand this, until i came over to your video. Thank you so much!
To be honest, I found MIT's lecture is a little bit better. The lecturer at MIT (Patrick Winston) provides more intuition behind the alpha beta pruning, while Prof Pieter Abbeel, although shows clear logic behind each step, does not give us reson for why you should do this, and the example is a bit complicated as well. So I personally recommend watching the MIT's AI lecture 6 at first, then watching this.That would be a perfect complement.
Anyone else following the General Game Playing course on Coursera? The Alpha-Beta Search was by far the most confusing one (because of lack of an example) so far in the lectures and this helped a lot.
+moraigna66 Also, there was a lack of a well defined system. No where it mentions how the alpha must only come from "above" for a minimizer but the same minimizer can use a beta from a previously explored "lower" branch and vice-versa for the maximizer.
I found this very helpful especially once the values of alpha and beta weren't just infinity and -infinity! I kept pausing the video and working through a couple steps then playing the video to see if I did them right, which really helped me grasp when you change the values of alpha and beta. Great video although it did take me a couple watches to fully get it!
Pretty good explanation. I think the hard to get part, for me, is the best path to the root concept. But if one works through some trees, I think it becomes more clear.
In the branch to the right, alpha is already set to 6. This means that the v should be set to 6 initially in the max-nodes, not -infinity as is shown in the video. If you find this hard, look up the minmax algorithm. Alpha-beta is built upon minmax
A little bit messy But still 100 times better than my University's AI lecturer My understandeing: When a maximizer is going to look at other children, check if it already has a value that is greater than the best minimizer value along its way to root, you can stop it because the only thing it can do is to make this value more than the current value, however the current value is already greater than the best minimizer value, so the upper minimizer will discard this node anyways. Same thing to a minimizer, if it discovers its value is already smaller than the best maximizer value, you can stop it from expanding, because the only thing it can do is to keep lowering that value and it will be discarded later.
Congratulations, very well explained. It was very usefull for me. I agree there is not much tutorials which explain this concept as u do. Again, congratulations and thank you man.
it makes sense, but classifying the variable as internal structure of a node would've simplified your explanation greatly, and explaining that there appears to be two forms of pruning, local pruning and greater pruning, local where the local scope of an object is taken into account, in the first instance of the prune shown in this video; and the second where the tactical decision to circumvent this option was taken because no matter what option the maximizer presented to the minimizer the minimizer would've taken the value 2 or lower; as thats where it seems you confused alot of people. note i'm not stating there are different prunes, they use the same strategy just the reasoning behind the prune is different which i've represented with different names.
At around 2:22 to 2:29, Pieter says "for that it checks the following conditions: it checks whether this value of 4, the current value here is higher than beta. If that's the case, then it doesn't need to look at any further children here..." He should have said "...higher than OR EQUAL TO beta."
a good student need not be a good teacher. to become a really good teacher, you need to spend 10 times more time thinking what is the best innovative way so that my students can grasp this concept easily.
Which part, or what aspects lost you? This stuff always seems like magic at first, but asking questions, discussion, and going t/ examples really helps. I've been trying a few problems before this video, and it really helped make some of the steps click.
It's well explained it even has to much info, like addition of minimizer at the (main) root. But if you don't understand this I suggest dodging any kind of algorithm based courses in the future
Why is the second child of the middle minimizer pruned? below as a child of minimizer is a maximizer. The v value of the maximizer between 0 and 1 is 1. So at the minimizer we have v = 1, because 1 is the better option than 2.
13:39. It can't be pruned because what happens if that 2 was a 7. When it gets to the minimizer, a 7 is lower than the 8. Therefore it wins, when it gets to the root, the 7 is greater than the 6 therefore the 7 wins. Thus it can't be pruned.
Great tutorial, really helped me. But why are alpha and beta passed to leaf nodes? Is this because they're passed before we know its a leaf when implemented?
6 hours before the exam and I'm studying now. Why am I like this
Thanks for the video! Even though this is a relatively simple algorithm, the only way to really understand it is to go step by step on a toy problem, like you did. Excellent job!
While examining the children of a maximizer, if v of maximizer > beta, prune the rest of the children.
While examining the children of a minimizer, if v of minimizer < alpha, prune the rest of the children.
💯
ty you cleared my doubts !
Some important notes:
1) only in a Max node can update the corresponding alpha, so does Min for beta.
2) v can only be returned up to its parent
3) alpha and beta can only be passed down from its parent
4) cut the current node from the tree whenever alpha >= beta
Thanks man that helped A LOT .
You saved my ass right before my A.I exam
+Dinh Hung Nguyen yeah
+Dinh Hung Nguyen
Attention please at minute 2:28 !!!
Hello :)
Thank you very much for your explaination.
It is very helpful.
The algorithm checks whether the value 4 is higher or equal !!! to beta.
Thank you, BR
i have an AI exam in a few hours lol
me 2
one advice: Keep it clean and clear !!
This video helped me understand pruning better thank you! Those of you complaining abt how messy it is, draw it yourself as he goes along.
A very good explanation. A lot of people are complaining about how this could be more simple, but look at the title, it shows you if how exactly algorithm would do this.
If you understand the minima concept, this is an awesome explanation and it really helped me a lot. Thank you
Very well detailed explanation of alpha beta pruning given Sir! You are really a good teacher, saved me from this confusing algo! I was tearing my hair getting to understand this, until i came over to your video. Thank you so much!
Worst teacher
why? You didnt understand the explanation? :(
Nope. Nada.
:-(
To be honest, I found MIT's lecture is a little bit better. The lecturer at MIT (Patrick Winston) provides more intuition behind the alpha beta pruning, while Prof Pieter Abbeel, although shows clear logic behind each step, does not give us reson for why you should do this, and the example is a bit complicated as well. So I personally recommend watching the MIT's AI lecture 6 at first, then watching this.That would be a perfect complement.
Thank you for sharing. I understand better now.
Yes you are absolutely right, he explains it much better!
Yup, watch the MIT lecture first then this one! Helps so much
exactly......followed your sequence ........helped a lot
Agree, you are correct. The lecture explained better than this guy. Maybe I was a dumb or something. Can't get this video's idea T^T
Anyone else following the General Game Playing course on Coursera?
The Alpha-Beta Search was by far the most confusing one (because of lack of an example) so far in the lectures and this helped a lot.
+moraigna66 Also, there was a lack of a well defined system. No where it mentions how the alpha must only come from "above" for a minimizer but the same minimizer can use a beta from a previously explored "lower" branch and vice-versa for the maximizer.
I know it makes sense and all, but it sure does take a few minutes for it to sink in and stick. Glad you went through the entire process.
This is a great explanation. I am shocked to see that some people disliked this video....
I found this very helpful especially once the values of alpha and beta weren't just infinity and -infinity! I kept pausing the video and working through a couple steps then playing the video to see if I did them right, which really helped me grasp when you change the values of alpha and beta. Great video although it did take me a couple watches to fully get it!
you dont know how much u saved me. thanks so much man
Thanks :) Currently working on this in my AI class and this helped a lot.
Excellent walk through!
Pretty good explanation. I think the hard to get part, for me, is the best path to the root concept. But if one works through some trees, I think it becomes more clear.
this makes a lot of sense if you've already seen this like at least a dozen times. if you're new, don't even waste time watching this
This video helped me very much with my Artificial Intelligence course. Thank you for taking the time to make this video.
In the branch to the right, alpha is already set to 6. This means that the v should be set to 6 initially in the max-nodes, not -infinity as is shown in the video. If you find this hard, look up the minmax algorithm. Alpha-beta is built upon minmax
Always initiate unexplored max node to -infinity
A little bit messy
But still 100 times better than my University's AI lecturer
My understandeing:
When a maximizer is going to look at other children, check if it already has a value that is greater than the best minimizer value along its way to root, you can stop it because the only thing it can do is to make this value more than the current value, however the current value is already greater than the best minimizer value, so the upper minimizer will discard this node anyways.
Same thing to a minimizer, if it discovers its value is already smaller than the best maximizer value, you can stop it from expanding, because the only thing it can do is to keep lowering that value and it will be discarded later.
Congratulations, very well explained. It was very usefull for me.
I agree there is not much tutorials which explain this concept as u do.
Again, congratulations and thank you man.
Thank you. This helped me a lot !!
it makes sense, but classifying the variable as internal structure of a node would've simplified your explanation greatly, and explaining that there appears to be two forms of pruning, local pruning and greater pruning, local where the local scope of an object is taken into account, in the first instance of the prune shown in this video; and the second where the tactical decision to circumvent this option was taken because no matter what option the maximizer presented to the minimizer the minimizer would've taken the value 2 or lower; as thats where it seems you confused alot of people. note i'm not stating there are different prunes, they use the same strategy just the reasoning behind the prune is different which i've represented with different names.
Clear as mud
Listen to the explanation around 09:00 . "A maximizer has a better option than two- six" which comes from the previous branch, incidentally.
best ever the best the most best fantastic explanation thank you prof.
Only this is clear and accurate tutorial i have found on the internet..today is my AI exam..thanks a lot to you.
This is the best video explanation on youtube for alpha beta pruning
No, it's not.
nope
th-cam.com/video/zp3VMe0Jpf8/w-d-xo.html&ab_channel=JohnLevine
way better
This was really helpful for me. Thank you!
Thanks a lot. Well explained!
I am not sure why others did not get it. Maybe if you first check the theory, then watch this, you should get it.
2:27
should be: current value is higher OR EQUAL than beta
You sir, are a GOD
wish i could hit like button 100 millions times
thanks man God bless yuh
peace
This is a really well explained video. Thanks Pieter
Thank u sir.I have an A.I exam tomorrow.
At around 2:22 to 2:29, Pieter says "for that it checks the following conditions: it checks whether this value of 4, the current value here is higher than beta. If that's the case, then it doesn't need to look at any further children here..."
He should have said "...higher than OR EQUAL TO beta."
Great video! I finally understood how alpha-beta pruning works! Thanx!
nice video,I can understand the notion now.
a good student need not be a good teacher.
to become a really good teacher, you need to spend 10 times more time thinking what is the best innovative way so that my students can grasp this concept easily.
Great video. Thank you.
5 rewatches later
Its making sense
Thanks, great explanation!
Pretty much how Dragon Ball tournament was organized
lmao. dbsuper sucked ballz
dragon ballz that is ;)
underrated comment ...
Thanks. Helps lots
Great video , very clear .
much appreciated, very thorough explanation yet clear and simple.
thanks a lot prof.
can't thank you enough!
Thanks very much!
Nice speech, could finally understand the concept with of this vid
This is brilliant, much appreciated sir.
that video kind of saved my ass. thank you. maybe you can explain when the cut is called a-cut and when it is calles b-cut
Great video, really helped! (I'm a junior majoring in AI and Robotics)
First you do this.
Then this.
Then magic.
More magic.
Done. Do you understand now?
are you kidding me?? or are you a kid in real??? best explained ? where is deep cutting you dumb??
Which part, or what aspects lost you? This stuff always seems like magic at first, but asking questions, discussion, and going t/ examples really helps. I've been trying a few problems before this video, and it really helped make some of the steps click.
It's well explained it even has to much info, like addition of minimizer at the (main) root. But if you don't understand this I suggest dodging any kind of algorithm based courses in the future
that's because the depth is only 4
😂😂
great video
Nice and simple! Thanks.
thanks that helped to give me the last hint to understand alpha beta pruning =)
Thank so much for saving my final *big thumbs up*
OMG tks sir! you save my life !
Such a messy explanation, I got more confused than I was before watching this video
Thank you, seriously
Awesome Explaination!
Crystal clear!
Wonderfully explained. Thanks a lot!
This really helped me understand alpha-beta minimax! Thank you so much!
Thanks this helps much
Thanks, you're great
thanks a ton! one of the best explanations :)
Thanks a lot my friend !
Thanks a lot, sir.
thank you so much
Thank you Thank you Thank you :)))
Very very helpful for Computer Engineer
Thank you professor.
Thank you very much for the video. It really helped me!
thank you
great work
Why is the second child of the middle minimizer pruned? below as a child of minimizer is a maximizer. The v value of the maximizer between 0 and 1 is 1. So at the minimizer we have v = 1, because 1 is the better option than 2.
it's well explained. thanks
bro cooked 11 years ago and He thought we wouldn't notice
Nice. Works for me.
Thanks a lot, very helpful
Great vedio!!! thankx a lot....
Minimisehuhhas an option 4:30
Got me cracking midst an exam revision session.
Thanks a ton!!! appreciate it.
VERY GOOD VIDEO!
Hint: if you watch this in reverse it is also difficult to understand
Geniusss!!!! You´v saved my work
amazing explanation!
Might be hard if you're learning alpha beta pruning for the first time but excellent for review.
got it! thanks a lot.
Sir,it would be better if you explain it in different slides ....it is very difficult to understand.
thank you sir!
Well!!
13:39. It can't be pruned because what happens if that 2 was a 7. When it gets to the minimizer, a 7 is lower than the 8. Therefore it wins, when it gets to the root, the 7 is greater than the 6 therefore the 7 wins.
Thus it can't be pruned.
Thank you so much !!
Great tutorial, really helped me. But why are alpha and beta passed to leaf nodes? Is this because they're passed before we know its a leaf when implemented?
thank for this tutorial
Very nice explanation. Thank you :)