Breadth First Search - Part 1

The simplest version of breadth-first search. This version doesn't use a visited set but still finds the shortest path from the start state to a goal state. Part 2 will show you how to use a visited set to potentially make the search more efficient.

[@Jajdjejwi28]

I wasted hours at my uni with professors that overcomplicated these search algorithms. You sir saved me big time!

[@Amirak254]

I relate

[@abhilamsal7185]

Breadth First Search applies goal test to each node when it is generated rather than when the node is selected for expansion. Therefore, once the algorithm determines shallowest goal node, it stops the search.
By following your explaination, the time complexity of BFS comes around in the order of O( b^ (n+1) ).
However, if the nodes were to be tested for goal nodes when they were generated rather than when selected for expansion, time complexity becomes O(b ^ n) since whole layer of nodes at depth n would be expanded before goal was detected.
b -- branching factor
n -- depth of shallowest goal node.

[@nerioamaral4045]

I love John's Lecture so helpful when it comes to explaining, I would love to have him as my AI Professor. anyways Thanks Dr John

[@mariagabrielagarciaarroyo4733]

Loving this channel, super well explained. Thank you!

[@raphaellmsousa]

Congratulations for your explanation! It was the best one for this topic for me!

[@limitless1692]

Wow simple and clear
Thank you Sir
Much appreciated :)

[@johnlevine2909]

You're very welcome!

[@benaya6]

the best explanation I found so far...thank you sir

[@kelvinmonari5512]

You are the best profesor ever

[@syntaxerror2144]

Amazing work brother!

[@franchello1105]

A couple of drawbacks to bring up. Not guaranteed to find the least costed path from S to a Goal State. Also would use a ton of storage to maintain all the current unexpanded nodes. Back in college we had to solve the canibal missionary problem using DFS, because the storage required would grow exponentially if BFS is used.

[@mustafaalshaqaq2303]

Thanks. This is very helpful.

[@yourfellowmuslimyagi2065]

Excellent explanation

[@yourdoom9868]

Thanks for the explanation sir!

[@ruixincheng6201]

great explanation!!! could you please upload more videos about Neural networks and probability problems? such as Bayes networks,approximate inference and CNN

[@fidelhernandez2322]

If let's say there is only one goal state (G1). Could I accept G1 as the goal state as soon as I add it to the frontier? By the way thank you for your clear explanation!

[@hosseinjavoon]

thank you it was great ❤🌹

[@abhirustagi6969]

thankyou sir!!! very well explained

[@AsomyTraiget]

Thanks

[@gabethebabe3840]

WOW great video!

[@tzaswit]

Thank you

[@jerushanmoodley2641]

Well explained

[@hashithakularatne3646]

don't we need to add all the visited letters for that path?

[@sknganga3633]

This guy saving my degree

[@amirhoxha6231]

smart fella

[@nadaali5]

thank u

[@kjohari_]

Missing your videos sir

[@mr.olorinthemaia]

radu cretulescu trece-ma la examen