arXiv subjects

Soumyajit Paul

Publications and source records attributed to Soumyajit Paul.

11 recordsLinked to original sources

arXiv2026

Automata-Theoretic Verification of Interval Markov Decision Processes

Interval Markov decision processes (IMDPs) provide a natural framework for modeling stochastic systems with uncertain transition probabilities, represented by probability intervals and resolved adversarially. Such uncertainty arises naturally, for example, when the transition model is learned from finite data or obtained through model-based reinforcement learning. In this paper, we study the automata-theoretic verification of IMDPs against rich temporal specifications, including all LTL specifications, by considering the broader class of ω-regular objectives. We show that classical automata-theoretic verification techniques extend to IMDPs, but with a sharp distinction determined by the structure of the transition intervals. For stable IMDPs, where either the upper bound is zero or the lower bound is strictly positive, verification reduces to ordinary MDP analysis and can be carried out using the standard automata used in that setting (good-for-MDP automata). For unstable IMDPs, where intervals may include zero while the upper bound is strictly positive, verification becomes game-like and requires automata whose nondeterminism can be resolved on the fly (good-for-games automata). Building on these insights, we develop algorithms for verifying ω-regular specifications over IMDPs and derive probabilistic guarantees when the interval model is learned from sampled data. The resulting framework enables principled verification of stochastic systems under probabilistic model uncertainty, connecting automata-based verification with data-driven stochastic modeling.

cs.FL↗

arXiv2026

On the Complexity of the Optimal Correlated Equilibria in Extensive-Form Games

A major open question in algorithmic game theory is whether normal-form correlated equilibria (NFCE) can be computed efficiently in succinct games such as extensive-form games. Motivated by this question, we study the associated Threshold problem: deciding whether there exists a correlated equilibrium whose value exceeds a given threshold. We prove that this problem is PSPACE-hard for NFCE in multiplayer extensive-form games with perfect recall, even for fixed thresholds. To contextualize this result, we also establish the complexity of the Threshold problem for Nash equilibria in this setting, showing it is ER-complete. These results uncover a surprising complexity reversal: while optimal correlated equilibria are computationally simpler than optimal Nash in normal-form games, the opposite holds in extensive-form games, where computing optimal correlated equilibria is strictly harder unless ER is equal to PSPACE. We also address a related question by von Stengel and Forges, who introduced the notions of extensive-form correlated equilibrium (EFCE) and agent-form correlated equilibrium (AFCE). They asked how difficult the Threshold problem is for AFCE; we answer this question by proving that it is NP-hard, even in two-player games without chance nodes. Complementing our hardness results, we establish tight complexity classifications for the Threshold problem across several correlated equilibrium concepts - including EFCE, AFCE, normal-form coarse, extensive-form coarse, and agent-form coarse correlated equilibria. For each of these solution concepts in multiplayer stochastic extensive-form games with perfect recall, we prove NP-completeness by providing matching NP upper bounds to the previously known hardness results. Finally, we also place the problem of computing arbitrary Nash Equilibria in extensive-form games in FixP, matching its complexity in normal-form games.

cs.GT↗

arXiv2026

Generalised Reachability Games

We study two-player zero-sum turn-based games played on graphs with multiple reachability objectives called generalised reachability games. In classic reachability games the goal of one player, Eve, is to visit a given target set of vertices, and that of the other player, Adam, is to prevent this. In generalised reachability games, the single target set is replaced with a family of target sets and the objective of Eve is to visit all of them in any order. We study the complexity of deciding the winner in two-player games with generalised reachability objectives. Our study reveals that an important parameter that determines the complexity of this problem is the size of the target sets. We first prove that deciding the winner in such games is PSPACE-complete, and the PSPACE lower bound holds even when the size of each target set is at most three. By contrast, we show that the problem is FPT in the number of target sets of size greater than one. Moreover, we consider the memory requirements for both players and give matching upper and lower bounds on the sizes of winning strategies. We also study optimisation variants of these games. For the optimisation problems, we show intractability for most interesting cases. Particularly, in contrast to the tractability of generalised reachability in the case with singleton target sets, the optimisation problem is coNP-hard when Eve tries to maximise the number of target sets that are visited. Tractability of this case can be recovered in a different optimisation setting where Eve is required to pledge a maximum sized subset of target sets that she can guarantee to visit.

cs.GT↗

arXiv2026

Social Welfare under Heterogeneous Time Preferences

In several socioeconomic-critical decision-making settings, such as fair resource allocation, climate policy, or AI alignment, multiple principals interact within a common arena. While it is well established that these principals may have differing preferences, decision-making under heterogeneous time preferences remains relatively unexplored. In particular, principals may weigh future outcomes differently and may derive distinct utilities from the same decisions. Motivated by such scenarios, we introduce the notion of heterogeneous time preferences in MDPs, where multiple principals possess distinct reward functions and apply different discount factors to future rewards. To compute meaningful decisions in such settings, an AI agent must rely on a notion of optimality that accounts for the preferences of all principals. We adopt a utilitarian notion of social welfare, defined as the sum of utilities accrued to all principals, and study the synthesis of agent strategies that maximise this welfare. Under heterogeneous time preferences, we show that optimal strategies are no longer positional, even when all principals receive identical rewards. Nevertheless, optimal strategies remain structurally simple: they can be realized as pure finite-memory counting strategies, require only polynomial memory in the system size, and can be synthesized in polynomial time. On the other hand, we show that deciding threshold questions for optimal positional strategies is NP-hard, exposing a poor trade-off: insisting on positional simplicity neither makes synthesis tractable nor preserves social welfare.

cs.GT↗

arXiv2026

Resolving Nondeterminism by Chance

History-deterministic automata are those in which nondeterministic choices can be correctly resolved stepwise: there is a strategy to select a continuation of a run given the next input letter so that if the overall input word admits some accepting run, then the constructed run is also accepting. Motivated by checking qualitative properties in probabilistic verification, we consider the setting where the resolver strategy can randomize and only needs to succeed with lower-bounded probability. We study the expressiveness of such stochastically-resolvable automata as well as consider the decision questions of whether a given automaton has this property. In particular, we show that it is undecidable to check if a given NFA is $λ$-stochastically resolvable. This problem is decidable for finitely-ambiguous automata. We also present complexity upper and lower bounds for several well-studied classes of automata for which this problem remains decidable.

cs.FL↗

arXiv2026

The Complexity of Games with Randomised Control

We study the complexity of solving two-player infinite duration games played on a fixed finite graph, where the control of a node is not predetermined but rather assigned randomly. In classic random-turn games, control of each node is assigned randomly every time the node is visited during a play. In this work, we study two natural variants of this where control of each node is assigned only once: (i) control is assigned randomly during a play when a node is visited for the first time and does not change for the rest of the play and (ii) control is assigned a priori before the game starts for every node by independent coin tosses and then the game is played. We investigate the complexity of computing the winning probability with three kinds of objectives-reachability, parity, and energy. We show that the qualitative questions on all variants and all objectives are NL-complete. For the quantitative questions, we show that deciding whether the maximiser can win with probability at least a given threshold for every objective is PSPACE-complete under the first mechanism, and that computing the exact winning probability for every objective is sharp-P-complete under the second. To complement our hardness results for the second mechanism, we propose randomised approximation schemes that efficiently estimate the winning probability for all three objectives, assuming a bounded number of parity colours and unary-encoded weights for energy objectives, and we empirically demonstrate their fast convergence.

cs.GT↗

arXiv2025

Generalised Reachability Games Revisited

Classic reachability games on graphs are zero-sum games, where the goal of one player, Eve, is to visit a vertex from a given target set, and that of other player, Adam, is to prevent this. Generalised reachability games, studied by Fijalkow and Horn, are a generalisation of reachability objectives, where instead of a single target set, there is a family of target sets and Eve must visit all of them in any order. In this work, we further study the complexity of solving two-player games on graphs with generalised reachability objectives. Our results are twofold: first, we provide an improved complexity picture for generalised reachability games, expanding the known tractable class from games in which all target sets are singleton to additionally allowing a logarithmic number of target sets of arbitrary size. Second, we study optimisation variants of generalised reachability with a focus on the size of the target sets. For these problems, we show intractability for most interesting cases. Particularly, in contrast to the tractability in the classic variant for singleton target sets, the optimisation problem is NP-hard when Eve tries to maximise the number of singleton target sets that are visited. Tractability can be recovered in the optimisation setting when all target sets are singleton by requiring that Eve pledges a maximum sized subset of target sets that she can guarantee to visit.

cs.GT↗

arXiv2025

Simplifying imperfect recall games

In games with imperfect recall, players may forget the sequence of decisions they made in the past. When players also forget whether they have already encountered their current decision point, they are said to be absent-minded. Solving one-player imperfect recall games is known to be NP-hard, even when the players are not absent-minded. This motivates the search for polynomial-time solvable subclasses. A special type of imperfect recall, called A-loss recall, is amenable to efficient polynomial-time algorithms. In this work, we present novel techniques to simplify non-absent-minded imperfect recall games into equivalent A-loss recall games. The first idea involves shuffling the order of actions, and leads to a new polynomial-time solvable class of imperfect recall games that extends A-loss recall. The second idea generalises the first one, by constructing a new set of action sequences which can be "linearly combined" to give the original game. The equivalent game has a simplified information structure, but it could be exponentially bigger in size (in accordance with the NP-hardness). We present an algorithm to generate an equivalent A-loss recall game with the smallest size.

cs.GT↗

arXiv2020

A Bridge between Polynomial Optimization and Games with Imperfect Recall

We provide several positive and negative complexity results for solving games with imperfect recall. Using a one-to-one correspondence between these games on one side and multivariate polynomials on the other side, we show that solving games with imperfect recall is as hard as solving certain problems of the first order theory of reals. We establish square root sum hardness even for the specific class of A-loss games. On the positive side, we find restrictions on games and strategies motivated by Bridge bidding that give polynomial-time complexity.

cs.GT↗

arXiv2019

Pushable chromatic number of graphs with degree constraints

Pushable homomorphisms and the pushable chromatic number $χ_p$ of oriented graphs were introduced by Klostermeyer and MacGillivray in 2004. They notably observed that, for any oriented graph $\overrightarrow{G}$, we have $χ_p(\overrightarrow{G}) \leq χ_o(\overrightarrow{G}) \leq 2 χ_p(\overrightarrow{G})$, where $χ_o(\overrightarrow{G})$ denotes the oriented chromatic number of $\overrightarrow{G}$. This stands as first general bounds on $χ_p$. This parameter was further studied in later works.This work is dedicated to the pushable chromatic number of oriented graphs fulfilling particular degree conditions. For all $Δ\geq 29$, we first prove that the maximum value of the pushable chromatic number of an oriented graph with maximum degree $Δ$ lies between $2^{\fracΔ{2}-1}$ and $(Δ-3) \cdot (Δ-1) \cdot 2^{Δ-1} + 2$ which implies an improved bound on the oriented chromatic number of the same family of graphs. For subcubic oriented graphs, that is, when $Δ\leq 3$, we then prove that the maximum value of the pushable chromatic number is~$6$ or~$7$. We also prove that the maximum value of the pushable chromatic number of oriented graphs with maximum average degree less than~$3$ lies between~$5$ and~$6$. The former upper bound of~$7$ also holds as an upper bound on the pushable chromatic number of planar oriented graphs with girth at least~$6$.

cs.DM↗

arXiv2016

Chromatic number of signed graphs with bounded maximum degree

A signed graph $ (G, Σ)$ is a graph positive and negative ($Σ$ denotes the set of negative edges). To re-sign a vertex $v$ of a signed graph $ (G, Σ)$ is to switch the signs of the edges incident to $v$. If one can obtain $ (G, Σ')$ by re-signing some vertices of $ (G, Σ)$, then $ (G, Σ) \equiv (G, Σ')$. A signed graphs $ (G, Σ)$ admits an homomorphism to $ (H, Λ)$ if there is a sign preserving vertex mapping from $(G,Σ')$ to $(H, Λ)$ for some $ (G, Σ) \equiv (G, Σ')$. The signed chromatic number $χ_{s}( (G, Σ))$ of the signed graph $(G, Σ)$ is the minimum order (number of vertices) of a signed graph $(H, Λ)$ such that $ (G, Σ)$ admits a homomorphism to $(H, Λ)$. For a family $ \mathcal{F}$ of signed graphs $χ_{s}(\mathcal{F}) = \text{max}_{(G,Σ) \in \mathcal{F}} χ_{s}( (G, Σ))$. We prove $2^{Δ/2-1} \leq χ_s(\mathcal{G}_Δ) \leq (Δ-1)^2. 2^{(Δ-1)} +2$ for all $Δ\geq 3$ where $\mathcal{G}_Δ$ is the family of connected signed graphs with maximum degree $Δ$. \end{abstract}

math.CO↗