Michaël Cadilhac

Shaull Almagor, Michaël Cadilhac, Asaf Shoham

Simon's factorization theorem is a celebrated tool in algebraic automata theory, providing bounded-depth decompositions of words with respect to morphisms into finite semigroups. We develop an analogue of Simon's theorem for \emph{forests} in the setting of forest algebras. In contrast with words, this presents a basic difficulty: recursively factoring a forest requires keeping track of where each subforest ``fits''. This difficulty ripples throughout the proof, and we overcome it by augmenting the free forest algebra and by developing a framework that supports recursive factorization of forests, along with its semantic implications. Our main result identifies a new semantic restriction on morphisms (called $\mathcal{R}$-alignment) which intuitively ensures that different ways of cutting a forest remain compatible (in a certain sense) at the semigroup level. Under this condition, we prove that every morphism admits decompositions of bounded depth. We also prove that without this restriction, there are morphisms for which no bounded-depth decomposition exists (under our notion of decomposition).

Michael Blondin, Michaël Cadilhac, Benjamin Courchesne et al.

Population protocols are a distributed computation model in which a collection of anonymous, finite-state agents interact in randomly chosen pairs and update their states according to a fixed transition function. The computation is defined by the eventual stabilization of the population to a consensus that represents the output. In practice, it is natural to allow each agent to carry a unique identifier and compare it with that of another agent before interacting. We model this extension by having agents be totally ordered and interactions between two agents to be fireable only if their pair of identifiers falls in some condition set. For instance, $\mathsf{PP}[<]$ allows for two agents to interact only if the first one appears before the second one. We study population protocols over ordered agents $\mathsf{PP}[N]$ where $N$ is a set of predicates available to restrict transition firing. We also study $\textsf{IO-PP}[N]$, the immediate observation fragment of $\mathsf{PP}[N]$ where only one agent changes state per interaction. Our main result is that $\textsf{IO-PP}[<]$ recognizes exactly the unambiguous star-free languages, which admits many other characterizations, such as two-variable first-order logic or two-way deterministic partially-ordered automata. We also provide a logic and an automaton model that fits in $\mathsf{PP}[<]$. We further show that if the successor predicate appears in a set $N$ of $\mathsf{NSPACE}(n)$-computable predicates, then $\textsf{IO-PP}[N]=\mathsf{PP}[N]=\mathsf{NSPACE}(n)$. Finally, we investigate the problem of deciding whether a given population protocol always stabilizes to a consensus. While this problem is decidable for unordered population protocols, we show that this is undecidable already for $\mathsf{PP}[<]$ and $\textsf{IO-PP}[+1]$, but conditionally decidable for $\textsf{IO-PP}[<]$.

Corentin Barloy, Michaël Cadilhac, Kyle Ockerlund

In single-core processors, when multiple processes execute concurrently, they are, in practice, intertwined by a scheduler as a single thread of execution. The language-theoretic operation that corresponds to this is the shuffle of two languages: in general, this is defined as the set of words obtained by interleaving words from the first and second language in an arbitrary fashion. It is well known that regular languages are closed under shuffles, while context-free languages (CFL) are not. Following an established line of research, this paper considers shufflings according to regular "trajectories", that is, subject to scheduling constraints expressed by an automaton. Unsurprisingly, some trajectories, such as "a word from the first language first, then a word from the second", allow for CFLs to be shuffled into CFLs, while some other trajectories do not. This paper provides a robust toolset to show that a given trajectory would always shuffle two nonregular CFLs into a nonCFL. In the case of deterministic CFLs (DCFLs), a salient trichotomy of trajectories depending on how they shuffle DCFLs is provided. These results are based on intricate expressiveness lemmas for CFLs and DCFLs of independent interest, the latter lemma relying on a recent result of Jančar and Šíma (MFCS'2021).

Ignacio Barros, Michaël Cadilhac, Guillermo A. Pérez

We study existential Presburger arithmetic extended with divisibility predicates (EPAD). Its satisfiability problem has long been known to be NP-hard, and has often been expected to lie in NP. We prove that it is PP-hard, ruling out this expectation unless NP=PP. This also implies \PP-hardness of satisfiability for positive Boolean combinations of word equations and length constraints. The lower bound is compatible with a strong form of Lipshitz-style simplification. We define a polynomial-time recognizable fragment, called \MergeAbs, in which the usual finite-quotient replacement of divisibility atoms can be repeated until no divisibility atom remains. Nevertheless, EPAD satisfiability is already PP-hard on this fully simplifiable fragment. The reduction starts from a threshold coefficient problem for a class of arithmetic circuits using only addition and shifts. The same systems used in the reduction also expose a limitation of normalization. A polynomial-size scaling family, indexed by $j$, forces an endpoint relation $v=(2^{2^j}+1)u$, and the natural finite-quotient simplification records it as one equation with coprime coefficients whose largest coefficient has bit-size $Θ(2^j)$.

Michaël Cadilhac, Guillermo A. Pérez, Marie van den Bogaard

Discounted-sum games provide a formal model for the study of reinforcement learning, where the agent is enticed to get rewards early since later rewards are discounted. When the agent interacts with the environment, she may regret her actions, realizing that a previous choice was suboptimal given the behavior of the environment. The main contribution of this paper is a PSPACE algorithm for computing the minimum possible regret of a given game. To this end, several results of independent interest are shown. (1) We identify a class of regret-minimizing and admissible strategies that first assume that the environment is collaborating, then assume it is adversarial---the precise timing of the switch is key here. (2) Disregarding the computational cost of numerical analysis, we provide an NP algorithm that checks that the regret entailed by a given time-switching strategy exceeds a given value. (3) We show that determining whether a strategy minimizes regret is decidable in PSPACE.