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.
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!
+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
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
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!
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.
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.
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.
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.
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
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?
Thanks a lot! I understand it much better now :) btw, i have a strange question: how do u write on the screen? is it a special digital pen? thanks again :)
How do you know the nodes are sorted like this such that the max/min knows to prune vs the other node being possibly lower on the same depth pair? (Like when 7 prunes 9 and it was swapped order)
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.
9:05 : How are we so sure that there is a lower value in the next branch, thus pruning this branch? I lack the understanding of how you come to that conclusion. If anyone could clear that up for me, I thank you so much.
+1stMusic Ah, it's not that there's a value less than 2 in the right branch, it's that the minimizer has has the option to choose 2 at that node (and possibly a lower value than 2 if it turns out that the right branch does have a lower value). Since the minimizer has the ability to choose a 2 (or maybe lower) in the main center branch, the maximizer would compare the value of 2 with the value of 6 to see which is higher. If the maximizer chooses the center branch then the minimizer would be able to force a game value of 2 or less; however if the maximizer chooses the left branch then the minimizer only has the ability to force a value as low as 6. thus the maximizer will only want to choose the left branch at that point, no matter what other unexplored values there are in the center branch. Hope that helped if you were still wondering about that.
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.
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.
💯
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!
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
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 .
If you understand the minima concept, this is an awesome explanation and it really helped me a lot. Thank you
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!
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.
one advice: Keep it clean and clear !!
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.
6 hours before the exam and I'm studying now. Why am I like this
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
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.
This is a great explanation. I am shocked to see that some people disliked this video....
Thanks :) Currently working on this in my AI class and this helped a lot.
you dont know how much u saved me. thanks so much man
This is a really well explained video. Thanks Pieter
wish i could hit like button 100 millions times
thanks man God bless yuh
peace
Thank you Thank you Thank you :)))
Very very helpful for Computer Engineer
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.
Great video! I finally understood how alpha-beta pruning works! Thanx!
This is brilliant, much appreciated sir.
Excellent walk through!
Thank you. This helped me a lot !!
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
This video helped me very much with my Artificial Intelligence course. Thank you for taking the time to make this video.
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.
Wonderfully explained. Thanks a lot!
Geniusss!!!! You´v saved my work
best ever the best the most best fantastic explanation thank you prof.
This was really helpful for me. Thank you!
thanks a ton! one of the best explanations :)
Thank you! Well done!
Thanx man!!that was seriously awesome..
Thanks, great explanation!
Thank you very much for the video. It really helped me!
This really helped me understand alpha-beta minimax! Thank you so much!
Thanks a ton!!! appreciate it.
Great video, really helped! (I'm a junior majoring in AI and Robotics)
Thanks for this screencast :)
much appreciated, very thorough explanation yet clear and simple.
Hey thanks for video if we apply alpha beta pruning right to left instead of left to right is it effect the result thank you
2:27
should be: current value is higher OR EQUAL than beta
Only this is clear and accurate tutorial i have found on the internet..today is my AI exam..thanks a lot to you.
amazing explanation!
Crystal clear!
Great video. Thank you.
Thank so much for saving my final *big thumbs up*
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
You sir, are a GOD
Nice speech, could finally understand the concept with of this vid
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?
OMG tks sir! you save my life !
Thanks a lot! I understand it much better now :)
btw, i have a strange question: how do u write on the screen? is it a special digital pen?
thanks again :)
Thanks. Helps lots
Awesome Explaination!
can't thank you enough!
Nice and simple! Thanks.
Beautiful.
Thank you professor.
Great video , very clear .
How do you know the nodes are sorted like this such that the max/min knows to prune vs the other node being possibly lower on the same depth pair? (Like when 7 prunes 9 and it was swapped order)
thanks a lot prof.
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
Thanks a lot, very helpful
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.
Thank you so much !!
Thanks, you're great
nice video,I can understand the notion now.
Thanks a lot my friend !
THANK YOU SO MUCH
Perfect!
Listen to the explanation around 09:00 . "A maximizer has a better option than two- six" which comes from the previous branch, incidentally.
best class ... thanks
Thanks very much!
thank you so much
5 rewatches later
Its making sense
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
Thanks this helps much
Thanks a lot, sir.
Hi, what if the first node is negative value? For example, if it is -5?
got it! thanks a lot.
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.
Very nice explanation. Thank you :)
it's well explained. thanks
9:05 : How are we so sure that there is a lower value in the next branch, thus pruning this branch? I lack the understanding of how you come to that conclusion.
If anyone could clear that up for me, I thank you so much.
+1stMusic Ah, it's not that there's a value less than 2 in the right branch, it's that the minimizer has has the option to choose 2 at that node (and possibly a lower value than 2 if it turns out that the right branch does have a lower value). Since the minimizer has the ability to choose a 2 (or maybe lower) in the main center branch, the maximizer would compare the value of 2 with the value of 6 to see which is higher. If the maximizer chooses the center branch then the minimizer would be able to force a game value of 2 or less; however if the maximizer chooses the left branch then the minimizer only has the ability to force a value as low as 6. thus the maximizer will only want to choose the left branch at that point, no matter what other unexplored values there are in the center branch.
Hope that helped if you were still wondering about that.
Tori Knorth Thanks, I got it!
thank you sir!
Well!!
Thank you very much!!!!
great work
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.
great video
Thank you so much for this video!!!!
Why I can't have a lecturer just like you!!!!!!
Thanks Professor
Thank you, really helpfull! :D
Great vedio!!! thankx a lot....
thank you
thanks that helped to give me the last hint to understand alpha beta pruning =)
Nice. Works for me.
I think that the 6th leaf, the number 2 is wrong, isnt it suposed to be the 6 a better option?