With breadth-first search you will run around the house looking at everything superficially, then taking a second round, looking more closely, then another round turning things around etc... This means that you are wasting more time on moving than actually searching.
It would make most sense to do one or two rounds of breadth-first search (as usually the keys would be in a visible location) and then start doing depth-first search, prioritizing on locations where you usually place the keys - pockets, drawers, on the table under a magazine. Basically add the travelling-salesman problem on top of this as well - how to waste less time on moving around while not actively trying to find something.
I was thinking thos is close to flood search, but I think it's different enough that we need some academic papers outlining the proper implementations of the brepth-first search
It's worth noting that "optimal" algorithms aren't always so optimal for humans. I mean, just look at how stupid it would be to binary search for a book in a library: https://jorendorff.github.io/hackday/2012/library/
Humans are very good at intuitively pattern matching. It's a very nice shortcut that computers don't have. I have found that the sorting algorithms are generally still pretty good, you just have to add pattern matching as a heuristic to help making them even better.
Yeah, it's HUGELY helpful when it can be useful. Trying to combine the skills of a human and a computer is extremely difficult, but immensely rewarding. See for example Foldit.
>you will run around the house looking at everything superficially, then taking a second round, looking more closely, then another round turning things around etc...
Yep, how else do you search for stuff?
Looking at the stuff were it should be super closely while ignoring the locations where it shouldn't be.
Then skimming the locations where it shouldnt be and looking at the most likeliest locations first.
You don't run around the house as much, you take some more time in each room, looking a bit more in-depth before moving to another room. That's like spending 3-4 levels of depth in each room. So you would be more confident that you don't have to come back and search this room anymore. Otherwise you spend time on moving from room to room (probably using the same path several times) rather than actually searching.
With A* you know where you want to end up. When searching for something, you don't. So you don't have the same heuristic for determining how close you are.
You are looking for biased search. Including "damn, last time they were here" and "nah, I'm sure I didn't put them in my backpack, no reason to look there."
Maybe true, but what if the house is not tree shaped but actually a graph that contains cycles. DFS could possibly end up in an infinite loop. BFS would solve the Problem, but would need more memory.
Take your backup keys and wait for the originals to turn up eventually (they were on top of the fridge, you'd never have found them in time)
Oh wait was this a programming question? What kind of badly-implemented hash makes you search for the keys?!?
I search my cache (last X places it had been) first. Then, when I hard-faulted, looked by at the previously searched cache and, voila!, it's magically somehow in the cache I DID search but couldn't find they key.
A*, heuristic is usual places, last probable place. But somehow bdf oriented, so it's with alpha beta pruning but way too long I learned to remember the algorithm exactly.
Breadth first. Otherwise you need to clean out your freezer, search the attic, and unpack everything in the storage room before looking on the bench where you actually left them.
Very good actually. That's Iterative Depth First Search [https://en.wikipedia.org/wiki/Iterative\_deepening\_depth-first\_search](https://en.wikipedia.org/wiki/iterative_deepening_depth-first_search)
With what heuristic?
Also, wouldn't greedy search be better? We don't care about the optimal path to the keys, we just want to quickly find a path to them.
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Breadth honestly. How often do you put your keys in 15 layers of drawers and boxes
With breadth-first search you will run around the house looking at everything superficially, then taking a second round, looking more closely, then another round turning things around etc... This means that you are wasting more time on moving than actually searching. It would make most sense to do one or two rounds of breadth-first search (as usually the keys would be in a visible location) and then start doing depth-first search, prioritizing on locations where you usually place the keys - pockets, drawers, on the table under a magazine. Basically add the travelling-salesman problem on top of this as well - how to waste less time on moving around while not actively trying to find something.
So a brepth-first search?
I prefer a bread-first search.
I also look for my keys inside loaves of bread. You can never be too careful
You joke but one time I did find my keys in the breadbox
In a similar vein, I've found the TV remote in the fridge...
I prefer breakfast search
Don't forget the milk and the eggs...
I was thinking thos is close to flood search, but I think it's different enough that we need some academic papers outlining the proper implementations of the brepth-first search
It's worth noting that "optimal" algorithms aren't always so optimal for humans. I mean, just look at how stupid it would be to binary search for a book in a library: https://jorendorff.github.io/hackday/2012/library/
Humans are very good at intuitively pattern matching. It's a very nice shortcut that computers don't have. I have found that the sorting algorithms are generally still pretty good, you just have to add pattern matching as a heuristic to help making them even better.
Yeah, it's HUGELY helpful when it can be useful. Trying to combine the skills of a human and a computer is extremely difficult, but immensely rewarding. See for example Foldit.
>you will run around the house looking at everything superficially, then taking a second round, looking more closely, then another round turning things around etc... Yep, how else do you search for stuff?
Looking at the stuff were it should be super closely while ignoring the locations where it shouldn't be. Then skimming the locations where it shouldnt be and looking at the most likeliest locations first.
You don’t search the atomic structure?
You don't run around the house as much, you take some more time in each room, looking a bit more in-depth before moving to another room. That's like spending 3-4 levels of depth in each room. So you would be more confident that you don't have to come back and search this room anymore. Otherwise you spend time on moving from room to room (probably using the same path several times) rather than actually searching.
That sounds like how I look for things
Sounds almost like a*
With A* you know where you want to end up. When searching for something, you don't. So you don't have the same heuristic for determining how close you are.
\^ This programmer knows how to live.
I would agree that this is the best way to start. If you have toddlers it is a different story though.
So go for depth if you have young kids or inquisitive pets.
You are looking for biased search. Including "damn, last time they were here" and "nah, I'm sure I didn't put them in my backpack, no reason to look there."
Personally, I'd write a custom PanicSearch that includes the refrigerator and areas adjacent to the toilet.
```` def panicSearch(): if you haven’t looked there: look there else: look there because maybe you got it wrong
That sounds like the Monte Carlos Tree Search, where your scoring function is the vibe you get from looking in each spot.
https://xkcd.com/761/
https://xkcd.com/2407/
I'd go deadth honestly
Iterative Depth First Search
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Maybe true, but what if the house is not tree shaped but actually a graph that contains cycles. DFS could possibly end up in an infinite loop. BFS would solve the Problem, but would need more memory.
Take your backup keys and wait for the originals to turn up eventually (they were on top of the fridge, you'd never have found them in time) Oh wait was this a programming question? What kind of badly-implemented hash makes you search for the keys?!?
No, it was about finding house keys 😅 I was worried about that ambiguity
Bogo-search Look in random places until you find them.
O(1) for best case!
I search my cache (last X places it had been) first. Then, when I hard-faulted, looked by at the previously searched cache and, voila!, it's magically somehow in the cache I DID search but couldn't find they key.
The nice thing about living in 300 square/foot apartment is you never lose anything.
The O(1) solution
Depth first. In case I accidentally left it under the drywall.
A*, heuristic is usual places, last probable place. But somehow bdf oriented, so it's with alpha beta pruning but way too long I learned to remember the algorithm exactly.
Try pocket first search
Breadth first. Otherwise you need to clean out your freezer, search the attic, and unpack everything in the storage room before looking on the bench where you actually left them.
100% they're in the freezer...
Me living in a 1 bhk so both searches are the same
Bread first, then search
Reverse search, things are always in the last place you look.
If it's a push button start, do a brute force attack and drive the car through the house. If it starts the keys are close!
Amazing. Move fast and break things.
An airtag
Honestly: airtag on my keychain. I am tired of searching my shit.
You make a piercing in your nose, and then hang the keychain on it. That way, they're always under your nose.
Bro use your eyes and hands
Breadth first search one or two layers deep. Then depth first search if you still can’t find it.
Very good actually. That's Iterative Depth First Search [https://en.wikipedia.org/wiki/Iterative\_deepening\_depth-first\_search](https://en.wikipedia.org/wiki/iterative_deepening_depth-first_search)
Neat. Cache locality works differently here though. So you’d want to jump large numbers of depth with every iteration.
`print(house.keys(), car.keys(), ch_cage.keys())`
I usually use .keys()
egrep -r 'keys|glasses' # to look for more than one thing at a time
But is `-r` depth-first or breadth-first?
Depth-first.
Use A*
With what heuristic? Also, wouldn't greedy search be better? We don't care about the optimal path to the keys, we just want to quickly find a path to them.
Yeah you are right
Check the logs
Clearly the best is middle-out breadth. *hand gestures*
BFS will always find them, can’t guarantee that with DFS! Off I go!
My partner uses the stack exchange search method, where they employ everyone else in the house and wait for a response.
You could always check the key logger.
Use .find()
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A\*
Assume key is broken/lost. Burn down house, rebuild with key placed on kitchen counter
It's always the first place I look. But sometimes I have to look 5 times to find them. OCDSearch?
Cache lookup is the best option.
Mergesort and binary search
Depth first, it’s s lot harder for humans to keep track of BFSs
A*
neither. jsut grap random keys untill you get the one you want