Luc Dartois

Luc Dartois, Pierre-Cyrille Héam, Ismaël Jecker et al.

We study bounded deviation of non-deterministic finite transducers under the Hamming distance: the bounded comparison problem asks, given two transducers and $k \in \mathbb{N}$, whether for every input the two transducers produce words at Hamming distance at most $k$. This problem is known to be decidable in polynomial time when $k$ is fixed, and in co-NP otherwise. We show that the problem is NL-complete when $k$ is fixed, co-NP-complete when $k$ is given in binary, and it is DP-complete to decide if the distance is exactly $k$. We also prove that if the two transducers have bounded comparison, then the maximal distance is at most quadratic in the size of both transducers, and that this bound is asymptotically tight. We prove the results on deviations problem, which asks similar questions on the distance of the pairs of input and output of a single transducer, and show that these two families of problems are logspace many-one equivalent.

Luc Dartois, Lê Thành Dũng Nguyên, Charles Peyrat

We investigate a natural generalization to trees of Hennie machines, a known automaton model for regular string functions. Tree-to-tree Hennie machines are tree-walking tree transducers with the ability to rewrite the node labels of their input tree, subject to a bounded visit restriction. Interestingly, they do not merely compute regular tree functions (i.e. MSO transductions), but a larger class of functions with linear size-to-height increase (LSHI). We prove that this class sits between LSHI macro tree transducers (MTTs) and MSO set interpretations. To argue for its robustness, we show that it is closed under precomposition (resp. postcomposition) by MTTs of linear size (resp. height) increase. As a consequence, it contains the entire composition hierarchy of MTTs of linear height increase; we also prove that this composition hierarchy is strict. Finally, we give an alternative characterization of this function class based on a lambda-calculus with linear types. The key difference with similar characterizations of MSO transductions is the use of additive tuples in the encoding of output trees. Our equivalence proof, using game semantics / geometry of interaction, is heavily inspired by an analogous result on higher-order recursion schemes.