EDBT 2026 Demo / reviewers in the wild / expert
Mohan Dantam
dblp:267/5675
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4ranked-venue papers
4as first author
4since 2021 · last 2026
0000-0002-4217-2981ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 4 · 4 first-author · 4 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Mean-Payoff-Parity and Lifting Strategies from MDPs to 2-Player Stochastic GamesabstractWe consider the strategy complexity (i.e., memory and randomization) of optimal strategies in turn-based 2-player zero-sum stochastic games. Results in [Gimbert and Kelmendi, 2023; Richard Mayr et al., 2021] show how to lift optimal memoryless strategies for shift-invariant inverse-submixing objectives from MDPs to 2-player stochastic games with an exponential increase in the number of memory modes. We show the corresponding lower bound, i.e., the extra exponential memory is required in general, even for randomized strategies. Moreover, we solve the strategy complexity of the well-studied mean-payoff-parity objective (MP > 0 ∩ EPAR) in 2-player stochastic games. This objective is also shift-invariant inverse-submixing, but easier than the worst case for this class. In MDPs, Maximizer has optimal memoryless randomized strategies, while optimal deterministic strategies require exponential memory. However, in stochastic games, optimal randomized strategies require, at least and at most, linear memory (equal to the number of even colors). Finally, we show that the different construction in [Gimbert and Zielonka, 2009; Patricia Bouyer et al., 2023] for lifting memoryless (resp. finite-memory) deterministic strategies from MDPs (resp. 1-player games) to 2-player games cannot be generalized even to memoryless randomized strategies. We construct a shift-invariant objective where Max and Min each have optimal memoryless randomized strategies in all MDPs, but optimal (randomized) Max strategies still require infinite memory in deterministic 2-player games. Mohan Dantam, Richard Mayr |
CONCUR | 1 |
| 2024 | Finite-Memory Strategies for Almost-Sure Energy-MeanPayoff Objectives in MDPsabstractWe consider finite-state Markov decision processes with the combined Energy-MeanPayoff objective. The controller tries to avoid running out of energy while simultaneously attaining a strictly positive mean payoff in a second dimension. We show that finite memory suffices for almost surely winning strategies for the Energy-MeanPayoff objective. This is in contrast to the closely related Energy-Parity objective, where almost surely winning strategies require infinite memory in general. We show that exponential memory is sufficient (even for deterministic strategies) and necessary (even for randomized strategies) for almost surely winning Energy-MeanPayoff. The upper bound holds even if the strictly positive mean payoff part of the objective is generalized to multidimensional strictly positive mean payoff. Finally, it is decidable in pseudo-polynomial time whether an almost surely winning strategy exists. Mohan Dantam, Richard Mayr |
ICALP | 1 |
| 2023 | Approximating the Value of Energy-Parity Objectives in Simple Stochastic GamesabstractWe consider simple stochastic games G with energy-parity objectives, a combination of quantitative rewards with a qualitative parity condition. The Maximizer tries to avoid running out of energy while simultaneously satisfying a parity condition. We present an algorithm to approximate the value of a given configuration in 2-NEXPTIME. Moreover, ε-optimal strategies for either player require at most O(2-EXP(|G|)⋅log(1/ε)) memory modes. Mohan Dantam, Richard Mayr |
MFCS | 1 |
| 2021 | On the decidability of reachability in continuous time linear time-invariant systemsabstractWe consider the decidability of state-to-state reachability in linear time-invariant control systems over continuous time. We analyze this problem with respect to the allowable control sets, which are assumed to be the image under a linear map of the unit hypercube (i.e. zonotopes). This naturally models bounded (sometimes called saturated) controls. Decidability of the version of the reachability problem in which control sets are affine subspaces of Rn is a fundamental result in control theory. Our first result is decidability in two dimensions (n = 2) if matrix A satisfies some spectral conditions and conditional decidablility in general. If the transformation matrix A is diagonal with rational entries (or rational multiples of the same algebraic number) then the reachability problem is decidable. If the transformation matrix A only has real eigenvalues, the reachability problem is conditionally decidable. The time-bounded reachability problem is conditionally decidable and unconditionally decidable in two dimensions. Some of our results rely on the decidability of certain logical theories --- namely the theory of the reals with exponential (Rexp) and with bounded sine (Rexp,sin)--- which have been proven decidable conditional on Schanuel's Conjecture --- a unifying conjecture in transcendence theory. We also obtain a hardness result for a mild generalization of the problem where the target is a simple set (hypercube of dimension n - 1 or hyperplane) instead of a point. In this case, we show that the problem is at least as hard as the Continuous Positivity problem if the control set is a singleton, or the Nontangential Continuous Positivity problem if the control set is [-1, 1]. Mohan Dantam, Amaury Pouly |
HSCC | 1 |