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There was some interesting work a while ago that solved sudoku using spiking neural networks too [1] (Fig. 5) with some nice associated theory.
[1] http://journals.plos.org/ploscompbiol/article?id=10.1371/jou...
You can solve sudoku with a SAT solver. You don't need neural networks, this was an assignment in cs 251 at UIC.
There are no easy way to solve SAT as the size of sudoku. Modern day SAT solvers are very large and contains years of experience, containing 10s of heuristics and complex structures. If we can simplify using neural network, I think it's a great step.
I find it interesting that when the NN fails to complete the whole puzzle, it seems to fail spectacularly (github.com/Kyubyong/sudoku#results) -- that is, there aren't a lot of good-partial attempts (90% or above). Does anyone know why this might be the case? Does the first wrong placement of a number in a gap essentially ruin the rest of the guesses?
I thought it was interesting that the "hard" category seemed to have more wrong answers than the 2 harder categories. Maybe there just aren't enough samples, but in the data shown in the table, it seems like a big difference.
You can solve sudoku with a SAT solver. You don't need neural networks, this was an assignment in cs 251 at UIC.
There are no easy way to solve SAT as the size of sudoku. Modern day SAT solvers are very large and contains years of experience, containing 10s of heuristics and complex structures. If we can simplify using neural network, I think it's a great step.
That is, yes, sat solvers have a lot of heuristics to speed them up. But, almost by definition, we understand those heuristics do can reason about the answers they give. NNs, however, are notorious for being incredibly opaque. They give his probabilistic answers, but my understanding is we really only trust them probabilistically and can't explain their answers.
You can solve sudoku with a SAT solver. You don't need neural networks, this was an assignment in cs 251 at UIC.
There are no easy way to solve SAT as the size of sudoku. Modern day SAT solvers are very large and contains years of experience, containing 10s of heuristics and complex structures. If we can simplify using neural network, I think it's a great step.
A neural network isn't simpler in any sense of the word. You might as well throw a simulated annealer at the problem.
That looks interesting. Sudoku is a fairly useful constraint satisfaction problem/testbed to test neural networks architectures on, since the most general version of sudoku is NP-complete. There was some interesting work a while ago that solved sudoku using spiking neural networks too [1] (Fig. 5) with some nice associated theory. [1] http://journals.plos.org/ploscompbiol/article?id=10.1371/jou...
Earlier quoted context omitted.
There are no easy way to solve SAT as the size of sudoku. Modern day SAT solvers are very large and contains years of experience, containing 10s of heuristics and complex structures. If we can simplify using neural network, I think it's a great step.
Well, it is really more of a exact cover problem, which can be solved quite simply and elegantly with Knuth's algorithm X. A neural network isn't simpler in any sense of the word. You might as well throw a simulated annealer at the problem.
Evidence that DNNs are somewhere between phase 2 and 3 of https://en.wikipedia.org/wiki/Hype_cycle