Steepest Ascent Hill Climbing Algorithm in Artificial Intelligence

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Steepest-Ascent Hill climbing: It first examines all the neighboring nodes and then selects the node closest to the solution state as of next node.
A Useful variation of Simple Hill Climbing considers all the moves from the current state and selects the best one as the next state. This method is called steepest ascent or gradient search.
Both simple and ascent hill climbing may fail to find a solution. They
may terminate by finding a state from which no better states can be generate instead of goal state.
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Опубликовано:

12 сен 2024

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Комментарии : 34

@dibakarupadhyay Год назад

Perfectly explained, love it. Thank you so much! Wish me luck for my exam tomorrow.

@ThinkXAcademy Год назад

Good luck for your exam💫

@narendramahalle3314 2 года назад

The explaination was great but there is one minor mistake from you is you have said steepest take lesser time than simple but it's not a case a steepest take more time than simple because it have to look for a next neghbouring states and choose best among them. Thus a simple is faster and take less time than steepest.

@ThinkXAcademy 2 года назад

That's why a good heuristic function is needed. In case on thousands of paths from source to destination steepest ascent takes lesser time as it ignores paths that are not optimal(similar to Greedy Approach).

@jaanu-cb9zm 2 месяца назад

Bro y 4 is spearate from the graph

@pradeepv6156 2 года назад

Yes, liked and subscribed for your efforts

@ThinkXAcademy 2 года назад

This motivates me to do more for our developers community💯

@bruh-moment-21 Год назад

Thank you very much. I like your efforts.

@KARAKAVALASAVINAY 3 года назад

clean and clear explanation brother

@ThinkXAcademy 3 года назад

Thanks brother👍make sure to share my content with others so this channel can grow⭐

@antoncagle4217 3 года назад

This series is exactly what I needed!

@uff_rani 2 года назад

thanks

@srinuguda 2 года назад

In simple hill climbing , how we are moving 2 to 3? As per algorithm, it will select best child and moves and no backtracking right? Do I am missing something?

@ThinkXAcademy 2 года назад

It is not actually moving from 2 to 3. It first checks whether it is a promising node or not to move to. The comparison is happening here, after comparing with all the child nodes the node which is the best one is the selected node.

@prathamkakkad 10 месяцев назад

I think one edge between 2 and 3 is missing that's all. The only difference between the hill climbing and the steep algo is that, in simple hill climbing a state is generated and if that new state is better than the current then it will be assigned as the current state. It doesn't guarantee that it is the best state among all the childs. So here also you can see that 2 has better hv than 1 then it is selected, although 3 was having best value but still. Now there is a missing edge in graph between 2 and 3, if it was there, which will make a move from 2 to 3 by comparing the hv of 3 with 2 and as 3s value is better then 3 will be assigned the current state, then 5 and then 7. So, there is one edge missing in the graph given

@kdm159 3 года назад

lip moment is not in sync is any one dubbing for u explianation or it my sys prblm

@gauravnaik3538 2 года назад

Dont watch his lip watch his explaination

@sanketbiradar7978 Год назад

Great video bro

@ThinkXAcademy Год назад

Thanks😄Share with other students also💯

@mhfarhadi4376 3 года назад

Clear explanation thank you!

@ThinkXAcademy 3 года назад

Keep Learning 😃 btw nice logo

@israaahmed7572 2 года назад

Thank you

@ThinkXAcademy 2 года назад

Keep learning and sharing our videos to help this channel grow👍🏻💯

@RoshanSingh-ce2qm 3 года назад

Great work bro

@ThinkXAcademy 3 года назад

Thank you✨Keep supporting by sharing and subscribing 😄

@angelitocatajan8449 2 года назад

how come 3 is the best one amoung 2 and 4? i think it should be 4

@ThinkXAcademy 2 года назад

On the curve you can observe that 3 is at higher position than 2 and 4. Hence it is more promising

@suyashrahatekar4964 Год назад

@@eeshasohail9518 I think it's just a convention to choose the lowest heuristic value but u can choose the highest one also , not sure bcoz i havent researched about it

@vivekraj883 2 года назад

well done alloooooo

@ThinkXAcademy 2 года назад

thanks🥔🥔

@priyapatel5497 Год назад

Bekkar

@AnkitSingh-cv4qu Год назад

bro in hill climb, we look for better heuristic value like suppose node-1 has hf value=4 and its one of child node have hf value=2 then it begins with that node as a current state, right??? And according to you if node-1 has hf=3 and suppose child node-3 has hf value=5 then it choose node-3 as a current state?? little confusion brother :-((

@atulshende6631 Год назад

hill climbing follows greedy approch , so we have to define what is greedy , let suppose if value is more ,then our problem will be optimal means we are greedy about higher value rather than lower value. so if node1 has hf 3 and node 3 has hf 5 then node3 will will be assigned to current state and (if node1 has hf value 4 and child node has hf value 2 then current state will not get change (limitation of hill climbing -> may be another nodes have higher value than current node))

@AnkitSingh-cv4qu Год назад

@@atulshende6631 great👏👏