@inproceedings{21411, abstract = {To achieve fast recovery from link failures, most modern communication networks feature fully decentralized fast re-routing mechanisms. These re-routing mechanisms rely on pre-installed static re-routing rules at the nodes (the routers), which depend only on local failure information, namely on the failed links incident to the node. Ideally, a network is perfectly resilient: the re-routing rules ensure that packets are always successfully routed to their destinations as long as the source and the destination are still physically connected in the underlying network after the failures. Unfortunately, there are examples where achieving perfect resilience is not possible. Surprisingly, only very little is known about the algorithmic aspect of when and how perfect resilience can be achieved. We investigate the computational complexity of analyzing such local fast re-routing mechanisms. Our main result is a negative one: we show that even checking whether a given set of static re-routing rules ensures perfect resilience is coNP-complete. Additionally, we investigate other fundamental variations of the problem. In particular, we show that our coNP-completeness proof also applies to scenarios where the re-routing rules have specific patterns (known as skipping in the literature). On the positive side, for scenarios where nodes do not have information about the link from which a packet arrived (the so-called in-port), we present a linear-time algorithm to realize perfect resilience whenever possible (which we show can also be determined in linear time). }, author = {Bentert, Matthias and Ceylan, Esra and Hübner, Valentin and Schmid, Stefan and Srba, Jiří}, booktitle = {29th International Conference on Principles of Distributed Systems}, isbn = {9783959774093}, issn = {1868-8969}, location = {Iaşi, Romania}, publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik}, title = {{Fast re-routing in networks: On the complexity of perfect resilience}}, doi = {10.4230/LIPIcs.OPODIS.2025.31}, volume = {361}, year = {2026}, } @article{21661, abstract = {Model checking undiscounted reachability and expected-reward properties on Markov decision processes (MDPs) are key for the verification of systems that act under uncertainty. Popular algorithms are policy iteration and variants of value iteration; in tool competitions, most participants rely on the latter. These algorithms generally need worst-case exponential time. However, the problem can equally be formulated as a linear programme, solvable in polynomial time. In this paper, we give a detailed overview of today’s state-of-the-art algorithms for MDP model checking with a focus on performance and correctness. We highlight their fundamental differences, and describe various optimizations and implementation variants. We experimentally compare floating-point and exact-arithmetic implementations of all algorithms on three benchmark sets using two probabilistic model checkers. Our results show that (optimistic) value iteration is a sensible default, but other algorithms are preferable in specific settings. This paper thereby provides a guide for MDP verification practitioners—tool builders and users alike.}, author = {Hartmanns, Arnd and Junges, Sebastian and Quatmann, Tim and Weininger, Maximilian}, issn = {1433-2787}, journal = {International Journal on Software Tools for Technology Transfer}, keywords = {Quantitative model checking, Markov decision process, Linear programming, Value iteration, Policy iteration}, publisher = {Springer Nature}, title = {{The revised practitioner’s guide to MDP model checking algorithms}}, doi = {10.1007/s10009-026-00848-y}, year = {2026}, } @inproceedings{21717, abstract = {Robust Markov Decision Processes (RMDPs) generalize classical MDPs that consider uncertainties in transition probabilities by defining a set of possible transition functions. An objective is a set of runs (or infinite trajectories) of the RMDP, and the value for an objective is the maximal probability that the agent can guarantee against the adversarial environment. We consider (a) reachability objectives, where given a target set of states, the goal is to eventually arrive at one of them; and (b) parity objectives, which are a canonical representation for ω-regular objectives. The qualitative analysis problem asks whether the objective can be ensured with probability 1. In this work, we study the qualitative problem for reachability and parity objectives on RMDPs without making any assumption over the structures of the RMDPs, e.g., unichain or aperiodic. Our contributions are twofold. We first present efficient algorithms with oracle access to uncertainty sets that solve qualitative problems of reachability and parity objectives. We then report experimental results demonstrating the effectiveness of our oracle-based approach on classical RMDP examples from the literature scaling up to thousands of states.}, author = {Asadi, Ali and Chatterjee, Krishnendu and Kafshdar Goharshadi, Ehsan and Karrabi, Mehrdad and Shafiee, Ali}, booktitle = {Proceedings of the 40th AAAI Conference on Artificial Intelligence}, issn = {2374-3468}, location = {Singapore, Singapore}, number = {43}, pages = {36137--36145}, publisher = {Association for the Advancement of Artificial Intelligence}, title = {{Qualitative analysis of ω-regular objectives on robust MDPs}}, doi = {10.1609/aaai.v40i43.40931}, volume = {40}, year = {2026}, } @inproceedings{21722, abstract = {Partially observable Markov decision processes (POMDPs) are a central model for uncertainty in sequential decision making. The most basic objective is the reachability objective, where a target set must be eventually visited, and the more general parity objectives can model all omega-regular specifications. For such objectives, the computational analysis problems are the following: (a) qualitative analysis that asks whether the objective can be satisfied with probability 1 (almost-sure winning) or probability arbitrarily close to 1 (limit-sure winning); and (b) quantitative analysis that asks for the approximation of the optimal probability of satisfying the objective. For general POMDPs, almost-sure analysis for reachability objectives is EXPTIME-complete, but limit-sure and quantitative analyses for reachability objectives are undecidable; almost-sure, limit-sure, and quantitative analyses for parity objectives are all undecidable. A special class of POMDPs, called revealing POMDPs, has been studied recently in several works, and for this subclass the almost-sure analysis for parity objectives was shown to be EXPTIME-complete. In this work, we show that for revealing POMDPs the limit-sure analysis for parity objectives is EXPTIME-complete, and even the quantitative analysis for parity objectives can be achieved in EXPTIME.}, author = {Asadi, Ali and Chatterjee, Krishnendu and Lurie, David and Saona Urmeneta, Raimundo J}, booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence}, issn = {2374-3468}, location = {Singapore, Singapore}, number = {43}, pages = {36146--36154}, publisher = {Association for the Advancement of Artificial Intelligence}, title = {{Revealing POMDPs: Qualitative and quantitative analysis for parity objectives}}, doi = {10.1609/aaai.v40i43.40932}, volume = {40}, year = {2026}, } @article{22102, abstract = {Differential privacy (DP) has established itself as one of the standards for ensuring privacy of individual data. However, reasoning about DP is a challenging and error-prone task, hence methods for formal verification and refutation of DP properties have received significant interest in recent years. In this work, we present a novel method for automated formal refutation of є-DP. Our method refutes є-DP by searching for a pair of inputs together with a non-negative function over outputs whose expected value on these two inputs differs by a significant amount. The two inputs and the non-negative function over outputs are computed simultaneously, by utilizing upper expectation supermartingales and lower expectation submartingales from probabilistic program analysis, which we leverage to introduce a sound and complete proof rule for є-DP refutation. To the best of our knowledge, our method is the first method for є-DP refutation to offer the following four desirable features: (1) it is fully automated, (2) it is applicable to stochastic mechanisms with sampling instructions from both discrete and continuous distributions, (3) it provides soundness guarantees, and (4) it provides semi-completeness guarantees. Our experiments show that our prototype tool SuperDP achieves superior performance compared to the state of the art and manages to refute є-DP for a number of challenging examples collected from the literature, including ones that were out of the reach of prior methods.}, author = {Chatterjee, Krishnendu and Kafshdar Goharshadi, Ehsan and Zikelic, Dorde}, issn = {2475-1421}, journal = {Proceedings of the ACM on Programming Languages}, keywords = {Static Program Analysis, Differential Privacy, Probabilistic Programming, Martingales}, number = {PLDI}, publisher = {ACM}, title = {{SuperDP: Differential privacy refutation via supermartingales}}, doi = {10.1145/3808296}, volume = {10}, year = {2026}, } @article{22101, abstract = {Evolutionary biology examines how the genetic and phenotypic composition of populations changes over time. An important goal is to determine the fixation probability of a single advantageous mutant that arises in a homogeneous population of N residents. Many real populations experience environmental gradients that cause mutations to be beneficial in some spatial regions but harmful in others. Here, we study the fixation probability of a mutant placed on a simple one-dimensional spatial structure that experiences such a gradient. The mutant’s fitness varies linearly from1 − s to 1 + s, whereas the resident fitness is constant and equal to 1. The existing literature suggests that such heterogeneity in the mutant’s fitness should lead to a decrease in its fixation probability. However, in this work, we find that small, non-negligible gradients (s < 1=√N) substantially increase the fixation probability,while larger gradients (s > (log N)/√N) substantially decrease it.Moreover, we quantify the strength of this phenomenon analytically and we precisely delimit the range of the gradients for which it occurs. Our computer simulations closely match those findings. Altogether, our results indicate that subjecting a simple population structure to natural environmental conditions can produce strong counterintuitive effects.}, author = {Svoboda, Jakub and Nemati, Hossein and Tkadlec, Josef and Kaveh, Kamran and Chatterjee, Krishnendu}, issn = {2041-1723}, journal = {Nature Communications}, publisher = {Springer Nature}, title = {{The effect of the fitness gradient on fixation probability}}, doi = {10.1038/s41467-026-71777-2}, volume = {17}, year = {2026}, } @misc{22134, abstract = {This artifact provides the source code, benchmarks, and scripts necessary to build and reproduce the experimental results for `SuperDP` (Accepted at PLDI 2026). It also includes instructions for running the tool on user-provided inputs.}, author = {Chatterjee, Krishnendu and Kafshdar Goharshadi, Ehsan and Zikelic, Dorde}, publisher = {Zenodo}, title = {{SuperDP: Differential Privacy Refutation via Supermartingales}}, doi = {10.5281/ZENODO.18930113}, year = {2026}, } @inproceedings{22920, abstract = {Two-player games on graphs are a classical framework for analyzing strategic decision making. In turn-based games, two players move a token along the edges of the graph, and the right to move the token is determined by the current vertex. In traditional bidding games - referred to as pure bidding games - the right to move the token is determined at each step through bidding; here we consider Richman bidding, where the winning player of a bid pays the losing player. The winner is decided based on a temporal or quantitative specification evaluated over the resulting infinite play. In this work, we combine turn-based games and pure bidding games into generalized bidding games, with player-1 vertices, player-2 vertices, and bidding vertices. This natural and simple generalization of bidding games has far-reaching consequences. First, we show that, as a model, generalized bidding games are more expressive than pure bidding games, and we provide several applications. Second, and most importantly, we show that generalized Richman bidding games are structurally equivalent to simple stochastic games, a well-studied model: they are linearly interreducible to each other. As was previously known, the special case of pure Richman bidding games corresponds to random-turn games. In other words, generalized bidding games extend pure bidding games in the same way that simple stochastic games extend random-turn games. We use this connection to solve generalized Richman bidding games for temporal (parity) and quantitative (mean-payoff and discounted-sum) specifications. From a computational perspective, we establish that generalized bidding games with parity and mean-payoff specifications retain the best known upper bounds for turn-based games and pure bidding games, namely NP∩coNP. Finally, we study a repair problem that asks whether bidding vertices can be assigned "owners" so as to bring the threshold budget required to win the game below a given target. This problem has direct applications in compositional policy synthesis for multi-objective settings, and we show it to be NP-complete.}, author = {Asadi, Ali and Henzinger, Thomas A and Kafshdar Goharshadi, Ehsan and Kebis, Pavol and Mallik, Kaushik}, booktitle = {37th International Conference on Concurrency Theory}, isbn = {9783959774475}, issn = {1868-8969}, keywords = {Bidding Games, Stochastic Games}, location = {Liverpool, United Kingdom}, publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik}, title = {{Generalized bidding games: Where bidding and stochastic games meet}}, doi = {10.4230/LIPIcs.CONCUR.2026.13}, volume = {391}, year = {2026}, } @inproceedings{22919, abstract = {Reachability is the most fundamental logical objective, yet it is notoriously difficult to learn in reinforcement learning settings: even for Markov decision processes, PAC learning of reachability is impossible without additional assumptions. This difficulty also holds in turn-based stochastic games (TBSGs), where two adversarial players interact on a finite state space. In this work, we consider turn-based stochastic games with reachability objectives. For such settings, adversarial learning, in which players are adversarial even in the learning phase, is impossible. Therefore, the goal is to consider learning, in which both players learn the unknown model together. In this spirit, previous literature on PAC learning in TBSGs considers (a) public information shared by both players; and (b) centralized learning, which means that players share the same learning algorithm. In this work, our contribution is two-fold. First, we relax these strong assumptions and ensure learning: (i) with private information not shared with the other player; and (ii) decentralized learning where the players do not share the same learning algorithm. To the best of our knowledge, this work is the first positive result for decentralized and private information learning of TBSGs with reachability objectives. Second, we introduce a game-theoretic generalization of the Expected Conditional Distance (ECD) parameter, which measures the expected length of reaching the target set. We establish a polynomial-sample complexity bound with respect to the number of states, actions, ECD parameter, and inverses of error tolerance and failure probability.}, author = {Asadi, Ali and Chatterjee, Krishnendu and Kebis, Pavol}, booktitle = {37th International Conference on Concurrency Theory}, isbn = {9783959774475}, issn = {1868-8969}, keywords = {formal methods, games and logic, logical aspects of AI, model checking}, location = {Liverpool, United Kingdom}, publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik}, title = {{PAC learning in turn-based stochastic games with reachability objectives: A decentralized private approach via expected conditional distance}}, doi = {10.4230/LIPIcs.CONCUR.2026.12}, volume = {391}, year = {2026}, } @inproceedings{22922, abstract = {In this note, we recall the history and our motivation behind the development of Strategy Logic and we discuss some of the work that ensued from its introduction.}, author = {Chatterjee, Krishnendu and Henzinger, Thomas A and Piterman, Nir}, booktitle = {37th International Conference on Concurrency Theory}, isbn = {9783959774475}, issn = {1868-8969}, keywords = {Strategy Logic, Games, Automata}, location = {Liverpool, United Kingdom}, publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik}, title = {{A look back at strategy logic}}, doi = {10.4230/LIPIcs.CONCUR.2026.6}, volume = {391}, year = {2026}, } @phdthesis{23079, abstract = {We study the computational aspects of the two central solution concepts of stochastic games played on finite graphs for an infinite number of steps: the value in two-player zero-sum games, and Nash equilibria in multi-player games. In both settings, exact solutions are in tractable. Fortwo-playerzero-sumconcurrentstochasticgames,valuescanbeirrational,and the associated decision problem is SQRT-SUM hard. For multi-player turn-based stochastic games,theequilibriumprobabilitiescanbeirrational,anddecidingtheconstrainedexistence of a Nash equilibrium is undecidable in the presence of memory, and ∃R-complete when the players are restricted to stationary strategies. Hence, in both settings, the natural computational problem is the approximation of the solution within an arbitrary additive error. In this thesis, we present improved complexity results and algorithms for such approximation problems. The first part of the thesis considers two-player zero-sum concurrent stochastic games with two classic objectives: (a) stateful-discounted objectives, which are similar to the classic discounted-sum objectives, but states are associated with different discount factors rather than a single discount factor; and (b) parity objectives, which are a canonical representation for ω-regular objectives. For stateful-discounted objectives, given an ordering of the discount factors, the limit value is the limit of the value of the stateful-discounted objectives, as the discount factors approach zero according to the given order. The value-approximation problem is known to be in EXPSPACE for the limit value of stateful-discounted objectives and in PSPACEforparityobjectives,andthebest-knownalgorithmsforbothproblemsareatleast exponential time, with an exponential dependence on the number of states and actions. Our main results for the value-approximation problem for the limit value of stateful-discounted objectives and parity objectives areas follows: (a)we establish TFNPNP complexity; and (b)we present algorithms that improve the dependency on the number of actions in the exponent from linear to logarithmic. In particular, if the number of states is constant, our algorithms run in polynomial time. The second part of the thesis considers multi-player turn-based stochastic games with terminal payoffs, where the players are restricted to stationary strategies. We study the approximate constrained existence problem of Nash equilibria: given a game that has a Nash equilibrium in which the payoff of every player lies in a given interval, compute a strategy profile that is an ε-Nash equilibrium and that satisfies the same constraints up to an additive error ε. Our main results are as follows: (a) we present an FNPNP algorithm for the problem; and(b)we establish that its decision version is NP-hard, already with two players. For the upperbound,weshowthatifaconstrainedNashequilibriumexists,thenthereexists one in which the non-zero probabilities are at least an inverse of a double-exponential in the i input size, and that such a strategy profile can be represented, up to an additive error ε, with floating-point numbers of polynomial size. We complement the upper bound with a lower bound on this approach: we present a game with only five players in which every constrained Nash equilibrium requires double-exponentially small probabilities. The two parts share a common technical toolkit: bounds on solutions of systems of polynomial inequalities that arise from real algebraic geometry, the floating-point representation of doubly-exponentially small numbers, and guess-and-verify procedures that resolve single-player games with an NP oracle.}, author = {Asadi, Ali}, issn = {2791-4585}, pages = {83}, publisher = {Institute of Science and Technology Austria}, title = {{Approximation of values and equilibria in stochastic games: Complexity and algorithms}}, doi = {10.15479/AT-ISTA-23079}, year = {2026}, } @article{21413, abstract = {We present a general framework for applying learning algorithms and heuristical guidance to the verification of Markov decision processes (MDPs). The primary goal of our techniques is to improve performance by avoiding an exhaustive exploration of the state space, instead focussing on particularly relevant areas of the system, guided by heuristics. Our work builds on the previous results of Br{á}zdil et al., significantly extending it as well as refining several details and fixing errors. The presented framework focuses on probabilistic reachability, which is a core problem in verification, and is instantiated in two distinct scenarios. The first assumes that full knowledge of the MDP is available, in particular precise transition probabilities. It performs a heuristic-driven partial exploration of the model, yielding precise lower and upper bounds on the required probability. The second tackles the case where we may only sample the MDP without knowing the exact transition dynamics. Here, we obtain probabilistic guarantees, again in terms of both the lower and upper bounds, which provides efficient stopping criteria for the approximation. In particular, the latter is an extension of statistical model-checking (SMC) for unbounded properties in MDPs. In contrast to other related approaches, we do not restrict our attention to time-bounded (finite-horizon) or discounted properties, nor assume any particular structural properties of the MDP.}, author = {Brázdil, Tomáš and Chatterjee, Krishnendu and Chmelik, Martin and Forejt, Vojtěch and Kretinsky, Jan and Kwiatkowska, Marta and Meggendorfer, Tobias and Parker, David and Ujma, Mateusz}, issn = {2751-4838}, journal = {TheoretiCS}, publisher = {TheoretiCS Foundation}, title = {{Learning algorithms for verification of Markov decision processes}}, doi = {10.46298/theoretics.25.10}, volume = {4}, year = {2025}, } @misc{21668, abstract = {This artifact allows to review and reproduce the experiments from the paper *A Revised Practitioner's Guide to MDP Model Checking Algorithms*. The package contains all original logfiles and derived data used to generate the plots as in the paper. Furthermore, the artifact contains the model checking tools `Storm` and `mcsta` in the version exercised in the paper, the used Docker container, as well as benchmark instances and execution scripts to reproduce the experiments. See also the artifact of the conference paper: https://zenodo.org/records/7509474}, author = {Hartmanns, Arnd and Junges, Sebastian and Quatmann, Tim and Weininger, Maximilian}, publisher = {Zenodo}, title = {{Benchmark data for the revised practitioner's guide to MDP model checking algorithms}}, doi = {10.5281/ZENODO.14500423}, year = {2025}, } @article{18529, abstract = {Temporal networks are obtained from time-dependent interactions among individuals, whereas the interactions can be emails, phone calls, face-to-face meetings, or work collaboration. In this article, a temporal game framework is established, in which interactions among rational individuals are embedded into two-player games in a time-dependent manner. This allows studying the time-dependent complexity and variability of interactions, and the way they affect prosocial behaviors. Based on this simple mathematical model, it is found that the level of cooperation is promoted when the time of collaboration is equally limited for every individual. This observation is confirmed by a series of systematic human experiments on over 1,400 subjects, forming a foundation for comprehensively describing human temporal interactions in collaboration. The research results reveal an important incentive for human cooperation, leading to a better understanding of a fascinating aspect of human nature in society.}, author = {Zhang, Yichao and Wang, Jiasheng and Wen, Guanghui and Guan, Jihong and Zhou, Shuigeng and Chen, Guanrong and Chatterjee, Krishnendu and Perc, Matjaz}, issn = {2327-4697}, journal = {IEEE Transactions on Network Science and Engineering}, number = {1}, pages = {4--12}, publisher = {IEEE}, title = {{Limitation of time promotes cooperation in structured collaboration systems}}, doi = {10.1109/TNSE.2024.3481434}, volume = {12}, year = {2025}, } @inproceedings{19375, abstract = {Despite the advances in probabilistic model checking, the scalability of the verification methods remains limited. In particular, the state space often becomes extremely large when instantiating parameterized Markov decision processes (MDPs) even with moderate values. Synthesizing policies for such huge MDPs is beyond the reach of available tools. We propose a learning-based approach to obtain a reasonable policy for such huge MDPs. The idea is to generalize optimal policies obtained by model-checking small instances to larger ones using decision-tree learning. Consequently, our method bypasses the need for explicit state-space exploration of large models, providing a practical solution to the state-space explosion problem. We demonstrate the efficacy of our approach by performing extensive experimentation on the relevant models from the quantitative verification benchmark set. The experimental results indicate that our policies perform well, even when the size of the model is orders of magnitude beyond the reach of state-of-the-art analysis tools.}, author = {Azeem, Muqsit and Chakraborty, Debraj and Kanav, Sudeep and Kretinsky, Jan and Mohagheghi, Mohammadsadegh and Mohr, Stefanie and Weininger, Maximilian}, booktitle = {26th International Conference on Verification, Model Checking, and Abstract Interpretation}, isbn = {9783031827020}, issn = {1611-3349}, location = {Denver, CO, United States}, pages = {97--120}, publisher = {Springer Nature}, title = {{1–2–3–Go! Policy synthesis for parameterized Markov decision processes via decision-tree learning and generalization}}, doi = {10.1007/978-3-031-82703-7_5}, volume = {15530}, year = {2025}, } @inproceedings{19445, abstract = {In reconfiguration, we are given two solutions to a graph problem, such as Vertex Cover or Dominating Set, with each solution represented by a placement of tokens on vertices of the graph. Our task is to reconfigure one into the other using small steps while ensuring the intermediate configurations of tokens are also valid solutions. The two commonly studied settings are Token Jumping and Token Sliding, which allows moving a single token to an arbitrary or an adjacent vertex, respectively. We introduce new rules that generalize Token Jumping, parameterized by the number of tokens allowed to move at once and by the maximum distance of each move. Our main contribution is identifying minimal rules that allow reconfiguring any possible given solution into any other for Independent Set, Vertex Cover, and Dominating Set. For each minimal rule, we also provide an efficient algorithm that finds a corresponding reconfiguration sequence. We further focus on the rule that allows each token to move to an adjacent vertex in a single step. This natural variant turns out to be the minimal rule that guarantees reconfigurability for Vertex Cover. We determine the computational complexity of deciding whether a (shortest) reconfiguration sequence exists under this rule for the three studied problems. While reachability for Vertex Cover is shown to be in P, finding a shortest sequence is shown to be NP-complete. For Independent Set and Dominating Set, even reachability is shown to be PSPACE-complete.}, author = {Křišťan, Jan Matyáš and Svoboda, Jakub}, booktitle = {19th International Conference and Workshops on Algorithms and Computation}, isbn = {9789819628445}, issn = {1611-3349}, location = {Chengdu, China}, pages = {244--265}, publisher = {Springer Nature}, title = {{Reconfiguration using generalized token jumping}}, doi = {10.1007/978-981-96-2845-2_16}, volume = {15411}, year = {2025}, } @article{19499, abstract = {Quantum hardware is inherently fragile and noisy. We find that the accuracy of traditional quantum error correction algorithms can be improved depending on the hardware. Given different hardware specifications, we automatically synthesize hardware-optimal algorithms for parity correction, qubit resetting, and GHZ (Greenberger–Horne–Zeilinger) state preparation. Using stochastic techniques from computer science, our method presents a computational tool to compute exact accuracy guarantees and synthesize optimal algorithms that are often different from traditional ones. We also show that improvements can be gained with respect to the Qiskit transpiler as we compute the hardware-optimal qubit mapping for the GHZ state-preparation problem.}, author = {Muroya Lei, Stefanie and Chatterjee, Krishnendu and Henzinger, Thomas A}, issn = {1091-6490}, journal = {Proceedings of the National Academy of Sciences}, number = {12}, publisher = {National Academy of Sciences}, title = {{Hardware-optimal quantum algorithms}}, doi = {10.1073/pnas.2419273122}, volume = {122}, year = {2025}, } @inproceedings{19600, abstract = {In this work, we explore route discovery in private payment channel networks. We first determine what “ideal" privacy for a routing protocol means in this setting. We observe that protocols achieving this strong privacy definition exist by leveraging Multi-Party Computation but they are inherently inefficient as they must involve the entire network. We then present protocols with weaker privacy guarantees but much better efficiency (involving only a small fraction of the nodes). The core idea is that both sender and receiver gossip a message which propagates through the network, and the moment any node in the network receives both messages, a path is found. In our first protocol the message is always sent to all neighbouring nodes with a delay proportional to the fees of that edge. In our second protocol the message is only sent to one neighbour chosen randomly with a probability proportional to its degree. We additionally propose a more realistic notion of privacy in order to measure the privacy leakage of our protocols in practice. Our realistic notion of privacy challenges an adversary that join the network with a fixed budget to create channels to guess the sender and receiver of a transaction upon receiving messages from our protocols. Simulations of our protocols on the Lightning network topology (for random transactions and uniform fees) show that 1) forming edges with high degree nodes is a more effective attack strategy for the adversary, 2) there is a tradeoff between the number of nodes involved in our protocols (privacy) and the optimality of the discovered path, and 3) our protocols involve a very small fraction of the network on average.}, author = {Avarikioti, Zeta and Bastankhah, Mahsa and Maddah-Ali, Mohammad Ali and Pietrzak, Krzysztof Z and Svoboda, Jakub and Yeo, Michelle X}, booktitle = {Computer Security. ESORICS 2024 International Workshops}, isbn = {9783031823480}, issn = {1611-3349}, location = {Bydgoszcz, Poland}, pages = {207--223}, publisher = {Springer Nature}, title = {{Route discovery in private payment channel networks}}, doi = {10.1007/978-3-031-82349-7_15}, volume = {15263}, year = {2025}, } @inproceedings{19666, abstract = {Markov decision processes (MDP) are a well-established model for sequential decision-making in the presence of probabilities. In *robust* MDP (RMDP), every action is associated with an *uncertainty set* of probability distributions, modelling that transition probabilities are not known precisely. Based on the known theoretical connection to stochastic games, we provide a framework for solving RMDPs that is generic, reliable, and efficient. It is *generic* both with respect to the model, allowing for a wide range of uncertainty sets, including but not limited to intervals, L1- or L2-balls, and polytopes; and with respect to the objective, including long-run average reward, undiscounted total reward, and stochastic shortest path. It is *reliable*, as our approach not only converges in the limit, but provides precision guarantees at any time during the computation. It is *efficient* because -- in contrast to state-of-the-art approaches -- it avoids explicitly constructing the underlying stochastic game. Consequently, our prototype implementation outperforms existing tools by several orders of magnitude and can solve RMDPs with a million states in under a minute.}, author = {Meggendorfer, Tobias and Weininger, Maximilian and Wienhöft, Patrick}, booktitle = {Proceedings of the 39th AAAI Conference on Artificial Intelligence}, issn = {2374-3468}, location = {Philadelphia, PA, United States}, number = {25}, pages = {26631--26641}, publisher = {Association for the Advancement of Artificial Intelligence}, title = {{Solving robust Markov decision processes: Generic, reliable, efficient}}, doi = {10.1609/aaai.v39i25.34865}, volume = {39}, year = {2025}, } @inproceedings{19742, abstract = {Statistical model checking estimates probabilities and expectations of interest in probabilistic system models by using random simulations. Its results come with statistical guarantees. However, many tools use unsound statistical methods that produce incorrect results more often than they claim. In this paper, we provide a comprehensive overview of tools and their correctness, as well as of sound methods available for estimating probabilities from the literature. For expected rewards, we investigate how to bound the path reward distribution to apply sound statistical methods for bounded distributions, of which we recommend the Dvoretzky-Kiefer-Wolfowitz inequality that has not been used in SMC so far. We prove that even reachability rewards can be bounded in theory, and formalise the concept of limit-PAC procedures for a practical solution. The modes SMC tool implements our methods and recommendations, which we use to experimentally confirm our results.}, author = {Budde, Carlos E. and Hartmanns, Arnd and Meggendorfer, Tobias and Weininger, Maximilian and Wienhöft, Patrick}, booktitle = {31st International Conference on Tools and Algorithms for the Construction and Analysis of Systems}, isbn = {9783031906428}, issn = {1611-3349}, location = {Hamilton, ON, Canada}, pages = {167--190}, publisher = {Springer Nature}, title = {{Sound statistical model checking for probabilities and expected rewards}}, doi = {10.1007/978-3-031-90643-5_9}, volume = {15696}, year = {2025}, }