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BP508 Bongard Problems with precise definitions vs. Bongard Problems with vague definitions.
BP1
BP3
BP4
BP6
BP13
BP23
BP31
BP67
BP72
BP103
BP104
BP210
BP292
BP312
BP321
BP322
BP324
BP325
BP329
BP334
BP344
BP348
BP367
BP368
BP376
BP384
BP386
BP389
BP390
BP391
BP523
BP527
BP557
BP558
BP559

. . .

BP2
BP9
BP10
BP11
BP12
BP14
BP62
BP119
BP148
BP364
BP393
BP505
BP508
BP509
BP511
BP524
BP571
BP812
BP813
BP847
BP865
BP894
BP895
BP939
BP1002
BP1111
BP1158
(edit; present; nest [left/right]; search; history)
COMMENTS

Bongard Problems sorted left have the keyword "precise" on the OEBP.

Bongard Problems sorted right have the keyword "fuzzy" on the OEBP.


In an precise Bongard Problem, any relevant example is either clearly sorted left, clearly sorted right, or clearly not sorted.

(All relevant examples clearly sorted either left or right is the keyword allsorted.)


How can it be decided whether or not a rule is precise? How can it be decided whether or not a rule classifies all "examples that are relevant"? There needs to be another rule to determine which examples the original rule intends to sort. Bongard Problems by design communicate ideas without fixing that context ahead of time. The label "precise" can only mean a Bongard Problem's rule seems precise to people who see it. (This "precise vs. fuzzy" Bongard Problem is fuzzy.)


In an precise "less than ___ vs. greater than ___" Bongard Problem (keyword spectrum), the division between the sides is usually an apparent threshold. For example, there is an intuitive threshold between acute and obtuse angles (see e.g. BP292).


As a rule of thumb, do not consider imperfections of hand drawn images (keyword ignoreimperfections) when deciding whether a Bongard Problem is precise or fuzzy. Just because one can draw a square badly does not mean "triangle vs. quadrilateral" (BP6) should be labelled fuzzy; similar vagueness arises in all hand-drawn Bongard Problems. (For Bongard Problems in which fine subtleties of drawings, including small imperfections, are meant to be considered, use the keyword perfect.)


Sometimes the way a Bongard Problem would sort certain examples is an unsolved problem in mathematics. (See e.g. BP820.) There is a precise criterion that has been used to verify each sorted example fits where it fits (some kind of mathematical proof); however, where some examples fit is still unknown. Whether or not such a Bongard Problem should be labelled "precise" might be debated.

(Technical note: some properties are known to be undecidable, and sometimes the decidability itself is unknown. See https://en.wikipedia.org/wiki/Decision_problem .)

(See the keyword proofsrequired.)

One way to resolve this ambiguity is to define "precise" as meaning that once people decide where an example belongs for a reason, they will all agree about it.


Sometimes the class of all examples in a Bongard Problem is imprecise, but, despite that, the rule sorting those examples is precise. Say, for some potential new example, it is unclear whether it should be included in the Bongard Problem at all, but, if it were included, it would be clear where it should be sorted (or that it should be left unsorted). A Bongard Problem like this can still be tagged "precise".

(If all examples are clearly sorted except for some example for which it is unclear whether it belongs to the class of relevant examples, the situation becomes ambiguous.)

On the other hand, sometimes the class of all examples is very clear, with an obvious boundary. (Keyword preciseworld.)


There is a subtle distinction to draw between Bongard Problems that are precise to the people making them and Bongard Problems that are precise to the people solving them. A Bongard Problem (particularly a non-allsorted one) might be labeled "precise" on the OEBP because the description and the listed ambiguous examples explicitly forbid sorting certain border cases; however, someone looking at the Bongard Problem without access to the OEBP page containing the definition would not be aware of this. It may or may not be obvious that certain examples were intentionally left out of the Bongard Problem. A larger collection of examples may make it more clear that a particularly blatant potential border case was left out intentionally.

CROSSREFS

See BP876 for the version with pictures of Bongard Problems instead of links to pages on the OEBP.

See both and neither for specific ways an example can be classified as unsorted in an "precise" Bongard Problem.

Adjacent-numbered pages:
BP503 BP504 BP505 BP506 BP507  *  BP509 BP510 BP511 BP512 BP513

KEYWORD

fuzzy, meta (see left/right), links, keyword, right-self, sideless

WORLD

bp [smaller | same | bigger]

AUTHOR

Aaron David Fairbanks

BP521 Bongard Problems with solution involving culture-specific information vs. Bongard Problems that an alien might be able to understand.
BP100
BP214
BP237
BP545
BP555
BP844
BP862
BP882
BP1000
BP1090
(edit; present; nest [left/right]; search; history)
COMMENTS

This is the keyword "culture" on the OEBP.

Any BP that has to do with the planet Earth belongs here.

CROSSREFS

Adjacent-numbered pages:
BP516 BP517 BP518 BP519 BP520  *  BP522 BP523 BP524 BP525 BP526

KEYWORD

meta (see left/right), links, keyword, sideless

WORLD

bp [smaller | same | bigger]

AUTHOR

Aaron David Fairbanks

BP858 Bongard Problems whose examples might be used to teach the rule of the solution vs. other Bongard Problems.
BP100
BP844
BP862
BP968
BP981
BP1049
BP1080
BP1083
BP1090
BP1153
(edit; present; nest [left/right]; search; history)
COMMENTS

Left examples have the keyword "teach" on the OEBP.


Sometimes instead of gauging somebody's ability to guess the pattern, a Bongard Problem might teach the pattern.


Consider a Bongard Problem whose left examples are images of a specific person's face; after seeing that Problem, one might be able to recognize that person.


A "teach" Bongard Problem (with a huge number of examples) could be taken as a training set for machine learning.


"Teach" BPs tend to be convoluted, arbitrary, cultural-knowledge-based (keyword culture), or they illustrate some insight that might be overlooked, perhaps mathematical (keyword math).

CROSSREFS

Adjacent-numbered pages:
BP853 BP854 BP855 BP856 BP857  *  BP859 BP860 BP861 BP862 BP863

KEYWORD

meta (see left/right), links, keyword

WORLD

bp [smaller | same | bigger]

AUTHOR

Aaron David Fairbanks

BP947 BPs where users are advised to only upload images in which the pixelation is not misleading vs. other "perfect" Bongard Problems that use pixelated images to closely approximate the actual intended shapes.
BP1
BP31
BP210
BP211
BP217
BP279
BP321
BP324
BP325
BP335
BP341
BP367
BP386
BP523
BP548
BP859
BP860
BP861
BP892
BP920
BP934
BP935
BP966
BP1008
BP1088
BP1089
BP1090
BP1093
BP1104
BP1131
BP1156
BP1161
BP1168
BP1183
BP344
BP559
BP564
BP912
BP937
BP949
BP965
(edit; present; nest [left/right]; search; history)
COMMENTS

Left examples have the keyword "pixelperfect" on the OEBP.


All examples here are perfect Bongard Problems. That is, subtle imperfections in images are meant to be considered.


When a Problem is tagged with "pixelperfect", users are reminded to make sure they do not upload images such that taking the pixelation into account would affect the sorting of that example. That is, the zoomed-in jagged blocky version of the picture should still fit the solution.


For example, in the examples of BP335, which is about tessellation, the pixels interlock properly.

CROSSREFS

Stable Bongard Problems are generally pixelperfect.

Adjacent-numbered pages:
BP942 BP943 BP944 BP945 BP946  *  BP948 BP949 BP950 BP951 BP952

KEYWORD

meta (see left/right), links, keyword, instruction

WORLD

perfect_bp [smaller | same | bigger]

AUTHOR

Leo Crabbe

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