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

[@mariagabrielagarciaarroyo4733]

Loving this channel, super well explained. Thank you!

[@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

[@raphaellmsousa]

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

[@benaya6]

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

[@yourdoom9868]

Thanks for the explanation sir!

[@syntaxerror2144]

Amazing work brother!

[@gabethebabe3840]

WOW great video!

[@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!

[@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.

[@abhirustagi6969]

thankyou sir!!! very well explained

[@mustafaalshaqaq2303]

Thanks. This is very helpful.

[@limitless1692]

Wow simple and clear Thank you Sir Much appreciated :)

[@johnlevine2909]

You're very welcome!

[@kelvinmonari5512]

You are the best profesor ever

[@yourfellowmuslimyagi2065]

Excellent explanation

[@user-ov1rt6pi7d]

thank you it was great ❤🌹

[@tzaswit]

Thank you

[@ruixincheng6201]

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

[@jerushanmoodley2641]

Well explained

[@nadaali5]

thank u

[@hashithakularatne3646]

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

[@amirhoxha6231]

smart fella

[@sknganga3633]

This guy saving my degree

[@kjohari_]

Missing your videos sir

[@mr.olorinthemaia]

radu cretulescu trece-ma la examen