On Learning Polynomial Recursive Programs
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Abstract (arXiv)
We introduce the class of P-finite automata. These are a generalisation of weighted automata, in which the weights of transitions can depend polynomially on the length of the input word. P-finite automata can also be viewed as simple tail-recursive programs in which the arguments of recursive calls can non-linearly refer to a variable that counts the number of recursive calls. The nomenclature is motivated by the fact that over a unary alphabet P-finite automata compute so-called P-finite sequences, that is, sequences that satisfy a linear recurrence with polynomial coefficients. Our main result shows that P-finite automata can be learned in polynomial time in Angluin's MAT exact learning model. This generalises the classical results that deterministic finite automata and weighted automata over a field are respectively polynomial-time learnable in the MAT model.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). First posted on arXiv 23 Oct 2023.
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- 2026SMT-Based Active Learning of Weighted Automatacited by 0
- 2026SMT-Based Active Learning of Weighted Automata8 May 2026