@inproceedings{5977, abstract = {We consider the stochastic shortest path (SSP)problem for succinct Markov decision processes(MDPs), where the MDP consists of a set of vari-ables, and a set of nondeterministic rules that up-date the variables. First, we show that several ex-amples from the AI literature can be modeled assuccinct MDPs. Then we present computationalapproaches for upper and lower bounds for theSSP problem: (a) for computing upper bounds, ourmethod is polynomial-time in the implicit descrip-tion of the MDP; (b) for lower bounds, we present apolynomial-time (in the size of the implicit descrip-tion) reduction to quadratic programming. Our ap-proach is applicable even to infinite-state MDPs.Finally, we present experimental results to demon-strate the effectiveness of our approach on severalclassical examples from the AI literature.}, author = {Chatterjee, Krishnendu and Fu, Hongfei and Goharshady, Amir and Okati, Nastaran}, booktitle = {Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence}, isbn = {9780999241127}, issn = {1045-0823}, location = {Stockholm, Sweden}, pages = {4700--4707}, publisher = {IJCAI}, title = {{Computational approaches for stochastic shortest path on succinct MDPs}}, doi = {10.24963/ijcai.2018/653}, volume = {2018}, year = {2018}, } @inproceedings{1194, abstract = {Termination is one of the basic liveness properties, and we study the termination problem for probabilistic programs with real-valued variables. Previous works focused on the qualitative problem that asks whether an input program terminates with probability~1 (almost-sure termination). A powerful approach for this qualitative problem is the notion of ranking supermartingales with respect to a given set of invariants. The quantitative problem (probabilistic termination) asks for bounds on the termination probability. A fundamental and conceptual drawback of the existing approaches to address probabilistic termination is that even though the supermartingales consider the probabilistic behavior of the programs, the invariants are obtained completely ignoring the probabilistic aspect. In this work we address the probabilistic termination problem for linear-arithmetic probabilistic programs with nondeterminism. We define the notion of {\em stochastic invariants}, which are constraints along with a probability bound that the constraints hold. We introduce a concept of {\em repulsing supermartingales}. First, we show that repulsing supermartingales can be used to obtain bounds on the probability of the stochastic invariants. Second, we show the effectiveness of repulsing supermartingales in the following three ways: (1)~With a combination of ranking and repulsing supermartingales we can compute lower bounds on the probability of termination; (2)~repulsing supermartingales provide witnesses for refutation of almost-sure termination; and (3)~with a combination of ranking and repulsing supermartingales we can establish persistence properties of probabilistic programs. We also present results on related computational problems and an experimental evaluation of our approach on academic examples. }, author = {Chatterjee, Krishnendu and Novotny, Petr and Zikelic, Djordje}, issn = {0730-8566}, location = {Paris, France}, number = {1}, pages = {145 -- 160}, publisher = {ACM}, title = {{Stochastic invariants for probabilistic termination}}, doi = {10.1145/3009837.3009873}, volume = {52}, year = {2017}, } @article{1294, abstract = {We study controller synthesis problems for finite-state Markov decision processes, where the objective is to optimize the expected mean-payoff performance and stability (also known as variability in the literature). We argue that the basic notion of expressing the stability using the statistical variance of the mean payoff is sometimes insufficient, and propose an alternative definition. We show that a strategy ensuring both the expected mean payoff and the variance below given bounds requires randomization and memory, under both the above definitions. We then show that the problem of finding such a strategy can be expressed as a set of constraints.}, author = {Brázdil, Tomáš and Chatterjee, Krishnendu and Forejt, Vojtěch and Kučera, Antonín}, journal = {Journal of Computer and System Sciences}, pages = {144 -- 170}, publisher = {Elsevier}, title = {{Trading performance for stability in Markov decision processes}}, doi = {10.1016/j.jcss.2016.09.009}, volume = {84}, year = {2017}, } @inproceedings{13160, abstract = {Transforming deterministic ω -automata into deterministic parity automata is traditionally done using variants of appearance records. We present a more efficient variant of this approach, tailored to Rabin automata, and several optimizations applicable to all appearance records. We compare the methods experimentally and find out that our method produces smaller automata than previous approaches. Moreover, the experiments demonstrate the potential of our method for LTL synthesis, using LTL-to-Rabin translators. It leads to significantly smaller parity automata when compared to state-of-the-art approaches on complex formulae.}, author = {Kretinsky, Jan and Meggendorfer, Tobias and Waldmann, Clara and Weininger, Maximilian}, booktitle = {Tools and Algorithms for the Construction and Analysis of Systems}, isbn = {9783662545768}, issn = {1611-3349}, location = {Uppsala, Sweden}, pages = {443--460}, publisher = {Springer}, title = {{Index appearance record for transforming Rabin automata into parity automata}}, doi = {10.1007/978-3-662-54577-5_26}, volume = {10205}, year = {2017}, } @article{1407, abstract = {We consider the problem of computing the set of initial states of a dynamical system such that there exists a control strategy to ensure that the trajectories satisfy a temporal logic specification with probability 1 (almost-surely). We focus on discrete-time, stochastic linear dynamics and specifications given as formulas of the Generalized Reactivity(1) fragment of Linear Temporal Logic over linear predicates in the states of the system. We propose a solution based on iterative abstraction-refinement, and turn-based 2-player probabilistic games. While the theoretical guarantee of our algorithm after any finite number of iterations is only a partial solution, we show that if our algorithm terminates, then the result is the set of all satisfying initial states. Moreover, for any (partial) solution our algorithm synthesizes witness control strategies to ensure almost-sure satisfaction of the temporal logic specification. While the proposed algorithm guarantees progress and soundness in every iteration, it is computationally demanding. We offer an alternative, more efficient solution for the reachability properties that decomposes the problem into a series of smaller problems of the same type. All algorithms are demonstrated on an illustrative case study.}, author = {Svoreňová, Mária and Kretinsky, Jan and Chmelik, Martin and Chatterjee, Krishnendu and Cěrná, Ivana and Belta, Cǎlin}, journal = {Nonlinear Analysis: Hybrid Systems}, number = {2}, pages = {230 -- 253}, publisher = {Elsevier}, title = {{Temporal logic control for stochastic linear systems using abstraction refinement of probabilistic games}}, doi = {10.1016/j.nahs.2016.04.006}, volume = {23}, year = {2017}, } @article{512, abstract = {The fixation probability is the probability that a new mutant introduced in a homogeneous population eventually takes over the entire population. The fixation probability is a fundamental quantity of natural selection, and known to depend on the population structure. Amplifiers of natural selection are population structures which increase the fixation probability of advantageous mutants, as compared to the baseline case of well-mixed populations. In this work we focus on symmetric population structures represented as undirected graphs. In the regime of undirected graphs, the strongest amplifier known has been the Star graph, and the existence of undirected graphs with stronger amplification properties has remained open for over a decade. In this work we present the Comet and Comet-swarm families of undirected graphs. We show that for a range of fitness values of the mutants, the Comet and Cometswarm graphs have fixation probability strictly larger than the fixation probability of the Star graph, for fixed population size and at the limit of large populations, respectively. }, author = {Pavlogiannis, Andreas and Tkadlec, Josef and Chatterjee, Krishnendu and Nowak, Martin}, issn = {2045-2322}, journal = {Scientific Reports}, number = {1}, publisher = {Nature Publishing Group}, title = {{Amplification on undirected population structures: Comets beat stars}}, doi = {10.1038/s41598-017-00107-w}, volume = {7}, year = {2017}, } @misc{5455, abstract = {A fundamental algorithmic problem at the heart of static analysis is Dyck reachability. The input is a graphwhere the edges are labeled with different types of opening and closing parentheses, and the reachabilityinformation is computed via paths whose parentheses are properly matched. We present new results for Dyckreachability problems with applications to alias analysis and data-dependence analysis. Our main contributions,that include improved upper bounds as well as lower bounds that establish optimality guarantees, are asfollows:First, we consider Dyck reachability on bidirected graphs, which is the standard way of performing field-sensitive points-to analysis. Given a bidirected graph withnnodes andmedges, we present: (i) an algorithmwith worst-case running timeO(m+n·α(n)), whereα(n)is the inverse Ackermann function, improving thepreviously knownO(n2)time bound; (ii) a matching lower bound that shows that our algorithm is optimalwrt to worst-case complexity; and (iii) an optimal average-case upper bound ofO(m)time, improving thepreviously knownO(m·logn)bound.Second, we consider the problem of context-sensitive data-dependence analysis, where the task is to obtainanalysis summaries of library code in the presence of callbacks. Our algorithm preprocesses libraries in almostlinear time, after which the contribution of the library in the complexity of the client analysis is only linear,and only wrt the number of call sites.Third, we prove that combinatorial algorithms for Dyck reachability on general graphs with truly sub-cubic bounds cannot be obtained without obtaining sub-cubic combinatorial algorithms for Boolean MatrixMultiplication, which is a long-standing open problem. Thus we establish that the existing combinatorialalgorithms for Dyck reachability are (conditionally) optimal for general graphs. We also show that the samehardness holds for graphs of constant treewidth.Finally, we provide a prototype implementation of our algorithms for both alias analysis and data-dependenceanalysis. Our experimental evaluation demonstrates that the new algorithms significantly outperform allexisting methods on the two problems, over real-world benchmarks.}, author = {Chatterjee, Krishnendu and Choudhary, Bhavya and Pavlogiannis, Andreas}, issn = {2664-1690}, pages = {37}, publisher = {IST Austria}, title = {{Optimal Dyck reachability for data-dependence and alias analysis}}, doi = {10.15479/AT:IST-2017-870-v1-1}, year = {2017}, } @misc{5456, abstract = {We present a new dynamic partial-order reduction method for stateless model checking of concurrent programs. A common approach for exploring program behaviors relies on enumerating the traces of the program, without storing the visited states (aka stateless exploration). As the number of distinct traces grows exponentially, dynamic partial-order reduction (DPOR) techniques have been successfully used to partition the space of traces into equivalence classes (Mazurkiewicz partitioning), with the goal of exploring only few representative traces from each class. We introduce a new equivalence on traces under sequential consistency semantics, which we call the observation equivalence. Two traces are observationally equivalent if every read event observes the same write event in both traces. While the traditional Mazurkiewicz equivalence is control-centric, our new definition is data-centric. We show that our observation equivalence is coarser than the Mazurkiewicz equivalence, and in many cases even exponentially coarser. We devise a DPOR exploration of the trace space, called data-centric DPOR, based on the observation equivalence. 1. For acyclic architectures, our algorithm is guaranteed to explore exactly one representative trace from each observation class, while spending polynomial time per class. Hence, our algorithm is optimal wrt the observation equivalence, and in several cases explores exponentially fewer traces than any enumerative method based on the Mazurkiewicz equivalence. 2. For cyclic architectures, we consider an equivalence between traces which is finer than the observation equivalence; but coarser than the Mazurkiewicz equivalence, and in some cases is exponentially coarser. Our data-centric DPOR algorithm remains optimal under this trace equivalence. Finally, we perform a basic experimental comparison between the existing Mazurkiewicz-based DPOR and our data-centric DPOR on a set of academic benchmarks. Our results show a significant reduction in both running time and the number of explored equivalence classes.}, author = {Chalupa, Marek and Chatterjee, Krishnendu and Pavlogiannis, Andreas and Sinha, Nishant and Vaidya, Kapil}, issn = {2664-1690}, pages = {36}, publisher = {IST Austria}, title = {{Data-centric dynamic partial order reduction}}, doi = {10.15479/AT:IST-2017-872-v1-1}, year = {2017}, } @inproceedings{551, abstract = {Evolutionary graph theory studies the evolutionary dynamics in a population structure given as a connected graph. Each node of the graph represents an individual of the population, and edges determine how offspring are placed. We consider the classical birth-death Moran process where there are two types of individuals, namely, the residents with fitness 1 and mutants with fitness r. The fitness indicates the reproductive strength. The evolutionary dynamics happens as follows: in the initial step, in a population of all resident individuals a mutant is introduced, and then at each step, an individual is chosen proportional to the fitness of its type to reproduce, and the offspring replaces a neighbor uniformly at random. The process stops when all individuals are either residents or mutants. The probability that all individuals in the end are mutants is called the fixation probability, which is a key factor in the rate of evolution. We consider the problem of approximating the fixation probability. The class of algorithms that is extremely relevant for approximation of the fixation probabilities is the Monte-Carlo simulation of the process. Previous results present a polynomial-time Monte-Carlo algorithm for undirected graphs when r is given in unary. First, we present a simple modification: instead of simulating each step, we discard ineffective steps, where no node changes type (i.e., either residents replace residents, or mutants replace mutants). Using the above simple modification and our result that the number of effective steps is concentrated around the expected number of effective steps, we present faster polynomial-time Monte-Carlo algorithms for undirected graphs. Our algorithms are always at least a factor O(n2/ log n) faster as compared to the previous algorithms, where n is the number of nodes, and is polynomial even if r is given in binary. We also present lower bounds showing that the upper bound on the expected number of effective steps we present is asymptotically tight for undirected graphs. }, author = {Chatterjee, Krishnendu and Ibsen-Jensen, Rasmus and Nowak, Martin}, booktitle = {Leibniz International Proceedings in Informatics}, isbn = {978-395977046-0}, location = {Aalborg, Denmark}, publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik}, title = {{Faster Monte Carlo algorithms for fixation probability of the Moran process on undirected graphs}}, doi = {10.4230/LIPIcs.MFCS.2017.61}, volume = {83}, year = {2017}, } @inproceedings{552, abstract = {Graph games provide the foundation for modeling and synthesis of reactive processes. Such games are played over graphs where the vertices are controlled by two adversarial players. We consider graph games where the objective of the first player is the conjunction of a qualitative objective (specified as a parity condition) and a quantitative objective (specified as a meanpayoff condition). There are two variants of the problem, namely, the threshold problem where the quantitative goal is to ensure that the mean-payoff value is above a threshold, and the value problem where the quantitative goal is to ensure the optimal mean-payoff value; in both cases ensuring the qualitative parity objective. The previous best-known algorithms for game graphs with n vertices, m edges, parity objectives with d priorities, and maximal absolute reward value W for mean-payoff objectives, are as follows: O(nd+1 . m . w) for the threshold problem, and O(nd+2 · m · W) for the value problem. Our main contributions are faster algorithms, and the running times of our algorithms are as follows: O(nd-1 · m ·W) for the threshold problem, and O(nd · m · W · log(n · W)) for the value problem. For mean-payoff parity objectives with two priorities, our algorithms match the best-known bounds of the algorithms for mean-payoff games (without conjunction with parity objectives). Our results are relevant in synthesis of reactive systems with both functional requirement (given as a qualitative objective) and performance requirement (given as a quantitative objective).}, author = {Chatterjee, Krishnendu and Henzinger, Monika H and Svozil, Alexander}, booktitle = {Leibniz International Proceedings in Informatics}, isbn = {978-395977046-0}, location = {Aalborg, Denmark}, publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik}, title = {{Faster algorithms for mean-payoff parity games}}, doi = {10.4230/LIPIcs.MFCS.2017.39}, volume = {83}, year = {2017}, } @inproceedings{553, abstract = {We consider two player, zero-sum, finite-state concurrent reachability games, played for an infinite number of rounds, where in every round, each player simultaneously and independently of the other players chooses an action, whereafter the successor state is determined by a probability distribution given by the current state and the chosen actions. Player 1 wins iff a designated goal state is eventually visited. We are interested in the complexity of stationary strategies measured by their patience, which is defined as the inverse of the smallest non-zero probability employed. Our main results are as follows: We show that: (i) the optimal bound on the patience of optimal and -optimal strategies, for both players is doubly exponential; and (ii) even in games with a single non-absorbing state exponential (in the number of actions) patience is necessary. }, author = {Chatterjee, Krishnendu and Hansen, Kristofer and Ibsen-Jensen, Rasmus}, booktitle = {Leibniz International Proceedings in Informatics}, isbn = {978-395977046-0}, location = {Aalborg, Denmark}, publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik}, title = {{Strategy complexity of concurrent safety games}}, doi = {10.4230/LIPIcs.MFCS.2017.55}, volume = {83}, year = {2017}, } @misc{5559, abstract = {Strong amplifiers of natural selection}, author = {Pavlogiannis, Andreas and Tkadlec, Josef and Chatterjee, Krishnendu and Nowak , Martin}, keywords = {natural selection}, publisher = {Institute of Science and Technology Austria}, title = {{Strong amplifiers of natural selection}}, doi = {10.15479/AT:ISTA:51}, year = {2017}, } @inbook{625, abstract = {In the analysis of reactive systems a quantitative objective assigns a real value to every trace of the system. The value decision problem for a quantitative objective requires a trace whose value is at least a given threshold, and the exact value decision problem requires a trace whose value is exactly the threshold. We compare the computational complexity of the value and exact value decision problems for classical quantitative objectives, such as sum, discounted sum, energy, and mean-payoff for two standard models of reactive systems, namely, graphs and graph games.}, author = {Chatterjee, Krishnendu and Doyen, Laurent and Henzinger, Thomas A}, booktitle = {Models, Algorithms, Logics and Tools}, editor = {Aceto, Luca and Bacci, Giorgio and Ingólfsdóttir, Anna and Legay, Axel and Mardare, Radu}, isbn = {978-3-319-63120-2}, issn = {0302-9743}, pages = {367 -- 381}, publisher = {Springer}, title = {{The cost of exactness in quantitative reachability}}, doi = {10.1007/978-3-319-63121-9_18}, volume = {10460}, year = {2017}, } @inproceedings{628, abstract = {We consider the problem of developing automated techniques for solving recurrence relations to aid the expected-runtime analysis of programs. The motivation is that several classical textbook algorithms have quite efficient expected-runtime complexity, whereas the corresponding worst-case bounds are either inefficient (e.g., Quick-Sort), or completely ineffective (e.g., Coupon-Collector). Since the main focus of expected-runtime analysis is to obtain efficient bounds, we consider bounds that are either logarithmic, linear or almost-linear (O(log n), O(n), O(n · log n), respectively, where n represents the input size). Our main contribution is an efficient (simple linear-time algorithm) sound approach for deriving such expected-runtime bounds for the analysis of recurrence relations induced by randomized algorithms. The experimental results show that our approach can efficiently derive asymptotically optimal expected-runtime bounds for recurrences of classical randomized algorithms, including Randomized-Search, Quick-Sort, Quick-Select, Coupon-Collector, where the worst-case bounds are either inefficient (such as linear as compared to logarithmic expected-runtime complexity, or quadratic as compared to linear or almost-linear expected-runtime complexity), or ineffective.}, author = {Chatterjee, Krishnendu and Fu, Hongfei and Murhekar, Aniket}, editor = {Majumdar, Rupak and Kunčak, Viktor}, isbn = {978-331963386-2}, location = {Heidelberg, Germany}, pages = {118 -- 139}, publisher = {Springer}, title = {{Automated recurrence analysis for almost linear expected runtime bounds}}, doi = {10.1007/978-3-319-63387-9_6}, volume = {10426}, year = {2017}, } @inproceedings{1009, abstract = {A standard objective in partially-observable Markov decision processes (POMDPs) is to find a policy that maximizes the expected discounted-sum payoff. However, such policies may still permit unlikely but highly undesirable outcomes, which is problematic especially in safety-critical applications. Recently, there has been a surge of interest in POMDPs where the goal is to maximize the probability to ensure that the payoff is at least a given threshold, but these approaches do not consider any optimization beyond satisfying this threshold constraint. In this work we go beyond both the “expectation” and “threshold” approaches and consider a “guaranteed payoff optimization (GPO)” problem for POMDPs, where we are given a threshold t and the objective is to find a policy σ such that a) each possible outcome of σ yields a discounted-sum payoff of at least t, and b) the expected discounted-sum payoff of σ is optimal (or near-optimal) among all policies satisfying a). We present a practical approach to tackle the GPO problem and evaluate it on standard POMDP benchmarks.}, author = {Chatterjee, Krishnendu and Novotny, Petr and Pérez, Guillermo and Raskin, Jean and Zikelic, Djordje}, booktitle = {Proceedings of the 31st AAAI Conference on Artificial Intelligence}, location = {San Francisco, CA, United States}, pages = {3725 -- 3732}, publisher = {AAAI Press}, title = {{Optimizing expectation with guarantees in POMDPs}}, volume = {5}, year = {2017}, } @inproceedings{1011, abstract = {Pushdown systems (PDSs) and recursive state machines (RSMs), which are linearly equivalent, are standard models for interprocedural analysis. Yet RSMs are more convenient as they (a) explicitly model function calls and returns, and (b) specify many natural parameters for algorithmic analysis, e.g., the number of entries and exits. We consider a general framework where RSM transitions are labeled from a semiring and path properties are algebraic with semiring operations, which can model, e.g., interprocedural reachability and dataflow analysis problems. Our main contributions are new algorithms for several fundamental problems. As compared to a direct translation of RSMs to PDSs and the best-known existing bounds of PDSs, our analysis algorithm improves the complexity for finite-height semirings (that subsumes reachability and standard dataflow properties). We further consider the problem of extracting distance values from the representation structures computed by our algorithm, and give efficient algorithms that distinguish the complexity of a one-time preprocessing from the complexity of each individual query. Another advantage of our algorithm is that our improvements carry over to the concurrent setting, where we improve the bestknown complexity for the context-bounded analysis of concurrent RSMs. Finally, we provide a prototype implementation that gives a significant speed-up on several benchmarks from the SLAM/SDV project.}, author = {Chatterjee, Krishnendu and Kragl, Bernhard and Mishra, Samarth and Pavlogiannis, Andreas}, editor = {Yang, Hongseok}, issn = {0302-9743}, location = {Uppsala, Sweden}, pages = {287 -- 313}, publisher = {Springer}, title = {{Faster algorithms for weighted recursive state machines}}, doi = {10.1007/978-3-662-54434-1_11}, volume = {10201}, year = {2017}, } @article{10416, abstract = {A fundamental algorithmic problem at the heart of static analysis is Dyck reachability. The input is a graph where the edges are labeled with different types of opening and closing parentheses, and the reachability information is computed via paths whose parentheses are properly matched. We present new results for Dyck reachability problems with applications to alias analysis and data-dependence analysis. Our main contributions, that include improved upper bounds as well as lower bounds that establish optimality guarantees, are as follows: First, we consider Dyck reachability on bidirected graphs, which is the standard way of performing field-sensitive points-to analysis. Given a bidirected graph with n nodes and m edges, we present: (i) an algorithm with worst-case running time O(m + n · α(n)), where α(n) is the inverse Ackermann function, improving the previously known O(n2) time bound; (ii) a matching lower bound that shows that our algorithm is optimal wrt to worst-case complexity; and (iii) an optimal average-case upper bound of O(m) time, improving the previously known O(m · logn) bound. Second, we consider the problem of context-sensitive data-dependence analysis, where the task is to obtain analysis summaries of library code in the presence of callbacks. Our algorithm preprocesses libraries in almost linear time, after which the contribution of the library in the complexity of the client analysis is only linear, and only wrt the number of call sites. Third, we prove that combinatorial algorithms for Dyck reachability on general graphs with truly sub-cubic bounds cannot be obtained without obtaining sub-cubic combinatorial algorithms for Boolean Matrix Multiplication, which is a long-standing open problem. Thus we establish that the existing combinatorial algorithms for Dyck reachability are (conditionally) optimal for general graphs. We also show that the same hardness holds for graphs of constant treewidth. Finally, we provide a prototype implementation of our algorithms for both alias analysis and data-dependence analysis. Our experimental evaluation demonstrates that the new algorithms significantly outperform all existing methods on the two problems, over real-world benchmarks.}, author = {Chatterjee, Krishnendu and Choudhary, Bhavya and Pavlogiannis, Andreas}, issn = {2475-1421}, journal = {Proceedings of the ACM on Programming Languages}, location = {Los Angeles, CA, United States}, number = {POPL}, publisher = {Association for Computing Machinery}, title = {{Optimal Dyck reachability for data-dependence and Alias analysis}}, doi = {10.1145/3158118}, volume = {2}, year = {2017}, } @article{10418, abstract = {We present a new proof rule for proving almost-sure termination of probabilistic programs, including those that contain demonic non-determinism. An important question for a probabilistic program is whether the probability mass of all its diverging runs is zero, that is that it terminates "almost surely". Proving that can be hard, and this paper presents a new method for doing so. It applies directly to the program's source code, even if the program contains demonic choice. Like others, we use variant functions (a.k.a. "super-martingales") that are real-valued and decrease randomly on each loop iteration; but our key innovation is that the amount as well as the probability of the decrease are parametric. We prove the soundness of the new rule, indicate where its applicability goes beyond existing rules, and explain its connection to classical results on denumerable (non-demonic) Markov chains.}, author = {Mciver, Annabelle and Morgan, Carroll and Kaminski, Benjamin Lucien and Katoen, Joost P}, issn = {2475-1421}, journal = {Proceedings of the ACM on Programming Languages}, location = {Los Angeles, CA, United States}, number = {POPL}, publisher = {Association for Computing Machinery}, title = {{A new proof rule for almost-sure termination}}, doi = {10.1145/3158121}, volume = {2}, year = {2017}, } @article{1065, abstract = {We consider the problem of reachability in pushdown graphs. We study the problem for pushdown graphs with constant treewidth. Even for pushdown graphs with treewidth 1, for the reachability problem we establish the following: (i) the problem is PTIME-complete, and (ii) any subcubic algorithm for the problem would contradict the k-clique conjecture and imply faster combinatorial algorithms for cliques in graphs.}, author = {Chatterjee, Krishnendu and Osang, Georg F}, issn = {0020-0190}, journal = {Information Processing Letters}, pages = {25 -- 29}, publisher = {Elsevier}, title = {{Pushdown reachability with constant treewidth}}, doi = {10.1016/j.ipl.2017.02.003}, volume = {122}, year = {2017}, } @article{1066, abstract = {Simulation is an attractive alternative to language inclusion for automata as it is an under-approximation of language inclusion, but usually has much lower complexity. Simulation has also been extended in two orthogonal directions, namely, (1) fair simulation, for simulation over specified set of infinite runs; and (2) quantitative simulation, for simulation between weighted automata. While fair trace inclusion is PSPACE-complete, fair simulation can be computed in polynomial time. For weighted automata, the (quantitative) language inclusion problem is undecidable in general, whereas the (quantitative) simulation reduces to quantitative games, which admit pseudo-polynomial time algorithms. In this work, we study (quantitative) simulation for weighted automata with Büchi acceptance conditions, i.e., we generalize fair simulation from non-weighted automata to weighted automata. We show that imposing Büchi acceptance conditions on weighted automata changes many fundamental properties of the simulation games, yet they still admit pseudo-polynomial time algorithms.}, author = {Chatterjee, Krishnendu and Henzinger, Thomas A and Otop, Jan and Velner, Yaron}, journal = {Information and Computation}, number = {2}, pages = {143 -- 166}, publisher = {Elsevier}, title = {{Quantitative fair simulation games}}, doi = {10.1016/j.ic.2016.10.006}, volume = {254}, year = {2017}, }