My question is that is there any general idea on how humans solve jumbled words? I know many people will say we match it against a commonly used words checklist mentally, but it is kind of vague. Is there any theory on this and how might an AI learn to do the same?

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    $\begingroup$ Jumble words would be a degree of pronoucable from the measure of disorder in a recognised pattern of the correct order and a reference to associated meaning in concept to storage in memory, the degree of disorder in communication would limit the knowledge of the composition and enumeration of the letters within the jumbled word, written communication would not suffer and it is not particularly intelligent to compose the absolute nature in possible combination and reference to known words from comparison and context of multiple returns in the rest of the communication and its solution. $\endgroup$ – Bobs Jun 30 '18 at 18:27
  • $\begingroup$ @Bobs just saying..How do you solve moderately tough jumbled words? $\endgroup$ – DuttaA Jun 30 '18 at 18:31
  • $\begingroup$ Well moderation is a measure of degree but its measure would come from knowledge of the correct order which is not a subject to the intelligence attempting to decipher it, and only a factor in the agent its resulted from and a product i guess undetermined by a nature of intelligence but a result of an anomaly in the pattern of intelligence we try to replicate and the word suffers in its communication from that agent. Degrees are attempted to be deal with fuzzy logic but even that is a product of reductionism of the whole to a picture which gives the two separate analysis. $\endgroup$ – Bobs Jun 30 '18 at 18:49

I remember a problem similar to this https://vladris.com/puzzles/facebook/puzzle_master/snack/breathalyzer.html this is given as a problem in facebook engineering puzzles.

But i doubt that it is not much of a machine learning/AI problem. you could implement an algorithm that converts each word into a set of its characters , then pick the word in your master list with minimum distance based on its character list .

Even when humans solve jumble we do it in a systematic/algorithmic way , if the jumbled word is not present in our memory we can't do anything otherwise we can solve the jumble.

But human brain can simply recognize scrabled words if some of the structure is retained and not fully scrambled like an anagram .

We generally index words based on the first syllable/character in our brain , like enter image description here or even this enter image description here

In the second picture we recognised the words even if they are not made of english characters because our brain doesn't scan the text character by character like "7-H " but treats it like an image.

So the model should not immediately classify the segmented characters but should find the "nearest" characters in every class that optimises the combined probability of characters of the word being one of the classes of words we have in our dictionary.

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  • $\begingroup$ Great answer..But in the first example if you notice, all the complex words are kept with their starting letter untouched..Even for machines finding nearest distance can be a tough job speaking from purely combinatorics viewpoint..As far as I solve I just try to speak different combinations of the letter mentally and suddenly it matches with pre heard sound wave and we have a match $\endgroup$ – DuttaA Jun 30 '18 at 18:29
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    $\begingroup$ for making the model more human-like which by the way suits the first example , you could include an "attention" mechanism that focuses more on the parts of the words which humans emphasize more when reading $\endgroup$ – riemann77 Jun 30 '18 at 18:30
  • $\begingroup$ Like vowels I guess $\endgroup$ – DuttaA Jun 30 '18 at 18:32
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    $\begingroup$ or it could be framed as a reinforcement learning problem , where the reward is +1 when the solved word is in the dictionary like in arxiv.org/pdf/1805.07470.pdf $\endgroup$ – riemann77 Jun 30 '18 at 18:36

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