Real-Time Vector Automata
\"Ozlem Salehi, Abuzer Yakary{\i}lmaz, A. C. Cem Say

TL;DR
This paper investigates the computational capabilities of real-time vector automata, which are finite automata enhanced with a vector that is multiplied by matrices at each step, and compares their language recognition power with related automata models.
Contribution
It introduces and analyzes the classes of languages recognized by deterministic, nondeterministic, and blind real-time vector automata, establishing their relationships with multicounter and multiplication automata.
Findings
Deterministic and nondeterministic vector automata recognize distinct language classes.
Blind vector automata have different computational power compared to non-blind versions.
The paper compares these classes with multicounter automata and generalized finite automata.
Abstract
We study the computational power of real-time finite automata that have been augmented with a vector of dimension k, and programmed to multiply this vector at each step by an appropriately selected matrix. Only one entry of the vector can be tested for equality to 1 at any time. Classes of languages recognized by deterministic, nondeterministic, and "blind" versions of these machines are studied and compared with each other, and the associated classes for multicounter automata, automata with multiplication, and generalized finite automata.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
