Michaël Cadilhac

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 $\textsf{IO-PP}[<]$.

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).

Andy Yang, Michaël Cadilhac, David Chiang

It has been observed that transformers with greater depth (that is, more layers) have more capabilities, but can we establish formally which capabilities are gained? We answer this question with a theoretical proof followed by an empirical study. First, we consider transformers that round to fixed precision except inside attention. We show that this subclass of transformers is expressively equivalent to the programming language C-RASP and this equivalence preserves depth. Second, we prove that deeper C-RASP programs are more expressive than shallower C-RASP programs, implying that deeper transformers are more expressive than shallower transformers (within the subclass mentioned above). The same is also proven for transformers with positional encodings (like RoPE and ALiBi). These results are established by studying a temporal logic with counting operators equivalent to C-RASP. Finally, we provide empirical evidence that our theory predicts the depth required for transformers without positional encodings to length-generalize on a family of sequential dependency tasks.

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.