A* Search: Heuristic Admissibility and Consistency: Are my estimates any good?

** Apologies for the low volume. Just turn it up **
Defines the concepts of admissibility and consistency with respect to heuristics used with the A* Search algorithm, and shows examples of the Manhattan and Euclidean distance heuristics.

[@BlakeEdwards333]

Thank you so much! This is an awesome explanation. Definitely had a light bulb moment with consistency with the help of your explanation.

[@shawnmofid7131]

Thank you so much for repeating what each symbol means. That is such a challenge for me when I am trying to learn on my own. I can follow your videos so well. You speak clear and with just the right speed for me to follow.

[@mushahidhussain1516]

I rarely comment on TH-cam videos, but mannnnnnn you are gifted. Thank you so much!

[@FunAi777]

underrated video, thank you

[@BeSharpInCSharp]

You are the only one who could tell me what these actually are.. thanks much

[@rismanjones]

your explanation on consistency is outstanding, great video!

[@AnuragSingh-dw9vf]

Thank you so much for this video. The explanation is crystal clear and easy to absorb. Thanks again.

[@victor-iglesias]

Finally, somebody who explains properly how to check if an heuristic is consistent or not. Thank you!

[@v2.055]

If the heuristic function is not consistent, we are not quarantined to find the rational/optimal solution. Same applies if we have an overestimating heuristic, it prevents us from exploring new and potentially better partial solutions. On the other hand underestimating can result in inefficiency, that is exploring dead ends or reaching nodes with added priorities, bigger than the actual shortes path. Eventually we will find the shortest path even after exploring undesirable nodes. Being optimistic leads to the goal, while being pessimistic prevents you from ever reaching it. That applies not only for A*, but irl

[@fadzisomavhundu9809]

Great video! thank you❤

[@hunkhazard1525]

00:01 Admissible heuristics never overestimate the true cost to the goal in A* search.
02:13 Heuristic Admissibility and Consistency in A* Search
04:40 Heuristic cost is an estimated cost in search algorithms.
06:48 Triangle inequality states that any edge of a triangle must be shorter than the sum of the two remaining sides.
08:53 A* search requires a good heuristic for efficiency.
10:51 Manhattan distance is a consistent heuristic in all locations
12:59 Using heuristics like Manhattan distance can save a lot of time in search algorithms.
14:56 Euclidean distance is a useful measure for finding distance between points with XY coordinates.

[@TheMcallist1]

Thanks Jason

[@KaaiSpeaksHisMind]

Thanks a lot, this was very useful.

[@shahinsalehi4780]

great video

[@kRySt4LGaMeR]

amazing video

[@yafesenessahiner4782]

Great video, thanks a lot

[@anisiahgrace2160]

Great video thz

[@ahmedessam5950]

Thank you very much this was very useful

[@pouyan1412]

Nice. Thanks a lot

[@renevank]

thank you!

[@aparocecilia8638]

thank you!!😃

[@casperdewith]

Good explanation. Thanks for providing the intuition.
I also think the example with the maze would have been more interesting if the goal would be in the top right. At the (4) intersection, you’d check both up and right to see if they are the right path.

[@heyitsme5408]

:3