arXiv subjects

Pranshu Gaba

Publications and source records attributed to Pranshu Gaba.

5 recordsLinked to original sources

arXiv2026

Sure-almost-sure and Sure-limit-sure Window Mean Payoff in Markov Decision Processes

Given rationals $\alpha$ and $\beta$, the sure-almost-sure problem for a threshold Boolean objective $\varphi$ in a Markov decision process (MDP) asks if one can simultaneously ensure that all outcomes of the MDP have $\varphi$-value at least $\alpha$ (i.e. sure $\alpha$ satisfaction) and with probability $1$ the outcome has $\varphi$-value at least $\beta$ (i.e. almost-sure $\beta$ satisfaction). The sure-limit-sure problem asks if for all $\varepsilon > 0$ one can simultaneously ensure that all outcomes have $\varphi$-value at least $\alpha$ and with probability at least $1 - \varepsilon$ the outcome has $\varphi$-value at least $\beta$. Moreover, if simultaneous satisfaction of objectives is possible, then one would also like to construct a strategy (for sure-almost-sure) or a family of strategies (for sure-limit-sure) that achieves this. In this paper, we solve the sure-almost-sure and sure-limit-sure problems for window mean-payoff objectives. The window mean-payoff objective strengthens the standard mean-payoff objective by requiring that eventually, from every point in the infinite run, the average payoff becomes greater than a given threshold within a finite window length. We study two variants of window mean payoff: in the fixed variant, the window length $\ell$ is given, while in the bounded variant, the length is not given but is required to be bounded throughout the run. We show that the sure-almost-sure problem and the sure-limit-sure problem are both in P for the fixed variant (if $\ell$ is given in unary) and are both in NP $\cap$ coNP for the bounded variant, matching the computational complexity of sure satisfaction and almost-sure satisfaction when considered separately for these objectives. We also give bounds for the memory requirement of winning strategies for all considered problems.

cs.GT↗

arXiv2025

Recognizing Numbers

The use of monoids in the study of word languages recognized by finite-state automata has been quite fruitful. In this work, we look at the same idea of "recognizability by finite monoids" for other monoids. In particular, we attempt to characterize recognizable subsets of various additive and multiplicative monoids over integers, rationals, reals, and complex numbers. While these recognizable sets satisfy properties such as closure under Boolean operations and inverse morphisms, they do not enjoy many of the nice properties that recognizable word languages do.

cs.FL↗

arXiv2025

Optimising expectation with guarantees for window mean payoff in Markov decision processes

The window mean-payoff objective strengthens the classical mean-payoff objective by computing the mean-payoff over a finite window that slides along an infinite path. Two variants have been considered: in one variant, the maximum window length is fixed and given, while in the other, it is not fixed but is required to be bounded. In this paper, we look at the problem of synthesising strategies in Markov decision processes that maximise the window mean-payoff value in expectation, while also simultaneously guaranteeing that the value is above a certain threshold. We solve the synthesis problem for three different kinds of guarantees: sure (that needs to be satisfied in the worst-case, that is, for an adversarial environment), almost-sure (that needs to be satisfied with probability one), and probabilistic (that needs to be satisfied with at least some given probability $p$). We show that for fixed window mean-payoff objective, all the three problems are in $\mathsf{PTIME}$, while for bounded window mean-payoff objective, they are in $\mathsf{NP} \cap \mathsf{coNP}$, and thus have the same complexity as for maximising the expected performance without any guarantee. Moreover, we show that pure finite-memory strategies suffice for maximising the expectation with sure and almost-sure guarantees, whereas, for maximising expectation with a probabilistic guarantee, randomised strategies are necessary in general.

cs.GT↗

arXiv2024

Expectation in Stochastic Games with Prefix-independent Objectives

Stochastic two-player games model systems with an environment that is both adversarial and stochastic. In this paper, we study the expected value of bounded quantitative prefix-independent objectives in the context of stochastic games. We show a generic reduction from the expectation problem to linearly many instances of the almost-sure satisfaction problem for threshold Boolean objectives. The result follows from partitioning the vertices of the game into so-called value classes where each class consists of vertices of the same value. Our procedure further entails that the memory required by both players to play optimally for the expectation problem is no more than the memory required by the players to play optimally for the almost-sure satisfaction problem for a corresponding threshold Boolean objective. We show the applicability of the framework to compute the expected window mean-payoff measure in stochastic games. The window mean-payoff measure strengthens the classical mean-payoff measure by computing the mean payoff over windows of bounded length that slide along an infinite path. We show that the decision problem to check if the expected window mean-payoff value is at least a given threshold is in UP $\cap$ coUP when the window length is given in unary.

cs.GT↗

arXiv2023

Stochastic Window Mean-Payoff Games

Stochastic two-player games model systems with an environment that is both adversarial and stochastic. The adversarial part of the environment is modeled by a player (Player 2) who tries to prevent the system (Player 1) from achieving its objective. We consider finitary versions of the traditional mean-payoff objective, replacing the long-run average of the payoffs by payoff average computed over a finite sliding window. Two variants have been considered: in one variant, the maximum window length is fixed and given, while in the other, it is not fixed but is required to be bounded. For both variants, we present complexity bounds and algorithmic solutions for computing strategies for Player 1 to ensure that the objective is satisfied with positive probability, with probability 1, or with probability at least $p$, regardless of the strategy of Player 2. The solution crucially relies on a reduction to the special case of non-stochastic two-player games. We give a general characterization of prefix-independent objectives for which this reduction holds. The memory requirement for both players in stochastic games is also the same as in non-stochastic games by our reduction. Moreover, for non-stochastic games, we improve upon the upper bound for the memory requirement of Player 1 and upon the lower bound for the memory requirement of Player 2.

cs.GT↗