@inproceedings{1140,
abstract = {Given a model of a system and an objective, the model-checking question asks whether the model satisfies the objective. We study polynomial-time problems in two classical models, graphs and Markov Decision Processes (MDPs), with respect to several fundamental -regular objectives, e.g., Rabin and Streett objectives. For many of these problems the best-known upper bounds are quadratic or cubic, yet no super-linear lower bounds are known. In this work our contributions are two-fold: First, we present several improved algorithms, and second, we present the first conditional super-linear lower bounds based on widely believed assumptions about the complexity of CNF-SAT and combinatorial Boolean matrix multiplication. A separation result for two models with respect to an objective means a conditional lower bound for one model that is strictly higher than the existing upper bound for the other model, and similarly for two objectives with respect to a model. Our results establish the following separation results: (1) A separation of models (graphs and MDPs) for disjunctive queries of reachability and Büchi objectives. (2) Two kinds of separations of objectives, both for graphs and MDPs, namely, (2a) the separation of dual objectives such as Streett/Rabin objectives, and (2b) the separation of conjunction and disjunction of multiple objectives of the same type such as safety, Büchi, and coBüchi. In summary, our results establish the first model and objective separation results for graphs and MDPs for various classical -regular objectives. Quite strikingly, we establish conditional lower bounds for the disjunction of objectives that are strictly higher than the existing upper bounds for the conjunction of the same objectives. © 2016 ACM.},
author = {Chatterjee, Krishnendu and Dvoák, Wolfgang and Henzinger, Monika and Loitzenbauer, Veronika},
booktitle = {Proceedings of the 31st Annual ACM/IEEE Symposium on Logic in Computer Science},
location = {New York, NY, USA},
pages = {197 -- 206},
publisher = {IEEE},
title = {{Model and objective separation with conditional lower bounds disjunction is harder than conjunction}},
doi = {10.1145/2933575.2935304},
year = {2016},
}
@inproceedings{1093,
abstract = {We introduce a general class of distances (metrics) between Markov chains, which are based on linear behaviour. This class encompasses distances given topologically (such as the total variation distance or trace distance) as well as by temporal logics or automata. We investigate which of the distances can be approximated by observing the systems, i.e. by black-box testing or simulation, and we provide both negative and positive results. },
author = {Daca, Przemyslaw and Henzinger, Thomas A and Kretinsky, Jan and Petrov, Tatjana},
location = {Quebec City; Canada},
publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik},
title = {{Linear distances between Markov chains}},
doi = {10.4230/LIPIcs.CONCUR.2016.20},
volume = {59},
year = {2016},
}
@inproceedings{1182,
abstract = {Balanced knockout tournaments are ubiquitous in sports competitions and are also used in decisionmaking and elections. The traditional computational question, that asks to compute a draw (optimal draw) that maximizes the winning probability for a distinguished player, has received a lot of attention. Previous works consider the problem where the pairwise winning probabilities are known precisely, while we study how robust is the winning probability with respect to small errors in the pairwise winning probabilities. First, we present several illuminating examples to establish: (a) there exist deterministic tournaments (where the pairwise winning probabilities are 0 or 1) where one optimal draw is much more robust than the other; and (b) in general, there exist tournaments with slightly suboptimal draws that are more robust than all the optimal draws. The above examples motivate the study of the computational problem of robust draws that guarantee a specified winning probability. Second, we present a polynomial-time algorithm for approximating the robustness of a draw for sufficiently small errors in pairwise winning probabilities, and obtain that the stated computational problem is NP-complete. We also show that two natural cases of deterministic tournaments where the optimal draw could be computed in polynomial time also admit polynomial-time algorithms to compute robust optimal draws.},
author = {Chatterjee, Krishnendu and Ibsen-Jensen, Rasmus and Tkadlec, Josef},
location = {New York, NY, USA},
pages = {172 -- 179},
publisher = {AAAI Press},
title = {{Robust draws in balanced knockout tournaments}},
volume = {2016-January},
year = {2016},
}
@article{1200,
author = {Hilbe, Christian and Traulsen, Arne},
journal = {Physics of Life Reviews},
pages = {29 -- 31},
publisher = {Elsevier},
title = {{Only the combination of mathematics and agent based simulations can leverage the full potential of evolutionary modeling: Comment on “Evolutionary game theory using agent-based methods” by C. Adami, J. Schossau and A. Hintze}},
doi = {10.1016/j.plrev.2016.10.004},
volume = {19},
year = {2016},
}
@inproceedings{1245,
abstract = {To facilitate collaboration in massive online classrooms, instructors must make many decisions. For instance, the following parameters need to be decided when designing a peer-feedback system where students review each others' essays: the number of students each student must provide feedback to, an algorithm to map feedback providers to receivers, constraints that ensure students do not become free-riders (receiving feedback but not providing it), the best times to receive feedback to improve learning etc. While instructors can answer these questions by running experiments or invoking past experience, game-theoretic models with data from online learning platforms can identify better initial designs for further improvements. As an example, we explore the design space of a peer feedback system by modeling it using game theory. Our simulations show that incentivizing students to provide feedback requires the value obtained from receiving a feedback to exceed the cost of providing it by a large factor (greater than 7). Furthermore, hiding feedback from low-effort students incentivizes them to provide more feedback.},
author = {Pandey, Vineet and Chatterjee, Krishnendu},
booktitle = {Proceedings of the ACM Conference on Computer Supported Cooperative Work},
location = {San Francisco, CA, USA},
number = {Februar-2016},
pages = {365 -- 368},
publisher = {ACM},
title = {{Game-theoretic models identify useful principles for peer collaboration in online learning platforms}},
doi = {10.1145/2818052.2869122},
volume = {26},
year = {2016},
}
@article{1322,
abstract = {Direct reciprocity is a major mechanism for the evolution of cooperation. Several classical studies have suggested that humans should quickly learn to adopt reciprocal strategies to establish mutual cooperation in repeated interactions. On the other hand, the recently discovered theory of ZD strategies has found that subjects who use extortionate strategies are able to exploit and subdue cooperators. Although such extortioners have been predicted to succeed in any population of adaptive opponents, theoretical follow-up studies questioned whether extortion can evolve in reality. However, most of these studies presumed that individuals have similar strategic possibilities and comparable outside options, whereas asymmetries are ubiquitous in real world applications. Here we show with a model and an economic experiment that extortionate strategies readily emerge once subjects differ in their strategic power. Our experiment combines a repeated social dilemma with asymmetric partner choice. In our main treatment there is one randomly chosen group member who is unilaterally allowed to exchange one of the other group members after every ten rounds of the social dilemma. We find that this asymmetric replacement opportunity generally promotes cooperation, but often the resulting payoff distribution reflects the underlying power structure. Almost half of the subjects in a better strategic position turn into extortioners, who quickly proceed to exploit their peers. By adapting their cooperation probabilities consistent with ZD theory, extortioners force their co-players to cooperate without being similarly cooperative themselves. Comparison to non-extortionate players under the same conditions indicates a substantial net gain to extortion. Our results thus highlight how power asymmetries can endanger mutually beneficial interactions, and transform them into exploitative relationships. In particular, our results indicate that the extortionate strategies predicted from ZD theory could play a more prominent role in our daily interactions than previously thought.},
author = {Hilbe, Christian and Hagel, Kristin and Milinski, Manfred},
journal = {PLoS One},
number = {10},
publisher = {Public Library of Science},
title = {{Asymmetric power boosts extortion in an economic experiment}},
doi = {10.1371/journal.pone.0163867},
volume = {11},
year = {2016},
}
@inproceedings{1324,
abstract = {DEC-POMDPs extend POMDPs to a multi-agent setting, where several agents operate in an uncertain environment independently to achieve a joint objective. DEC-POMDPs have been studied with finite-horizon and infinite-horizon discounted-sum objectives, and there exist solvers both for exact and approximate solutions. In this work we consider Goal-DEC-POMDPs, where given a set of target states, the objective is to ensure that the target set is reached with minimal cost. We consider the indefinite-horizon (infinite-horizon with either discounted-sum, or undiscounted-sum, where absorbing goal states have zero-cost) problem. We present a new and novel method to solve the problem that extends methods for finite-horizon DEC-POMDPs and the RTDP-Bel approach for POMDPs. We present experimental results on several examples, and show that our approach presents promising results. Copyright },
author = {Chatterjee, Krishnendu and Chmelik, Martin},
booktitle = {Proceedings of the Twenty-Sixth International Conference on International Conference on Automated Planning and Scheduling},
location = {London, United Kingdom},
pages = {88 -- 96},
publisher = {AAAI Press},
title = {{Indefinite-horizon reachability in Goal-DEC-POMDPs}},
volume = {2016-January},
year = {2016},
}
@inproceedings{1325,
abstract = {We study graphs and two-player games in which rewards are assigned to states, and the goal of the players is to satisfy or dissatisfy certain property of the generated outcome, given as a mean payoff property. Since the notion of mean-payoff does not reflect possible fluctuations from the mean-payoff along a run, we propose definitions and algorithms for capturing the stability of the system, and give algorithms for deciding if a given mean payoff and stability objective can be ensured in the system.},
author = {Brázdil, Tomáš and Forejt, Vojtěch and Kučera, Antonín and Novotny, Petr},
location = {Quebec City, Canada},
publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik},
title = {{Stability in graphs and games}},
doi = {10.4230/LIPIcs.CONCUR.2016.10},
volume = {59},
year = {2016},
}
@inproceedings{1326,
abstract = {Energy Markov Decision Processes (EMDPs) are finite-state Markov decision processes where each transition is assigned an integer counter update and a rational payoff. An EMDP configuration is a pair s(n), where s is a control state and n is the current counter value. The configurations are changed by performing transitions in the standard way. We consider the problem of computing a safe strategy (i.e., a strategy that keeps the counter non-negative) which maximizes the expected mean payoff. },
author = {Brázdil, Tomáš and Kučera, Antonín and Novotny, Petr},
location = {Chiba, Japan},
pages = {32 -- 49},
publisher = {Springer},
title = {{Optimizing the expected mean payoff in Energy Markov Decision Processes}},
doi = {10.1007/978-3-319-46520-3_3},
volume = {9938},
year = {2016},
}
@inproceedings{1327,
abstract = {We consider partially observable Markov decision processes (POMDPs) with a set of target states and positive integer costs associated with every transition. The traditional optimization objective (stochastic shortest path) asks to minimize the expected total cost until the target set is reached. We extend the traditional framework of POMDPs to model energy consumption, which represents a hard constraint. The energy levels may increase and decrease with transitions, and the hard constraint requires that the energy level must remain positive in all steps till the target is reached. First, we present a novel algorithm for solving POMDPs with energy levels, developing on existing POMDP solvers and using RTDP as its main method. Our second contribution is related to policy representation. For larger POMDP instances the policies computed by existing solvers are too large to be understandable. We present an automated procedure based on machine learning techniques that automatically extracts important decisions of the policy allowing us to compute succinct human readable policies. Finally, we show experimentally that our algorithm performs well and computes succinct policies on a number of POMDP instances from the literature that were naturally enhanced with energy levels. },
author = {Brázdil, Tomáš and Chatterjee, Krishnendu and Chmelik, Martin and Gupta, Anchit and Novotny, Petr},
booktitle = {Proceedings of the 15th International Conference on Autonomous Agents and Multiagent Systems},
location = {Singapore},
pages = {1465 -- 1466},
publisher = {ACM},
title = {{Stochastic shortest path with energy constraints in POMDPs}},
year = {2016},
}
@article{1333,
abstract = {Social dilemmas force players to balance between personal and collective gain. In many dilemmas, such as elected governments negotiating climate-change mitigation measures, the decisions are made not by individual players but by their representatives. However, the behaviour of representatives in social dilemmas has not been investigated experimentally. Here inspired by the negotiations for greenhouse-gas emissions reductions, we experimentally study a collective-risk social dilemma that involves representatives deciding on behalf of their fellow group members. Representatives can be re-elected or voted out after each consecutive collective-risk game. Selfish players are preferentially elected and are hence found most frequently in the "representatives" treatment. Across all treatments, we identify the selfish players as extortioners. As predicted by our mathematical model, their steadfast strategies enforce cooperation from fair players who finally compensate almost completely the deficit caused by the extortionate co-players. Everybody gains, but the extortionate representatives and their groups gain the most.},
author = {Milinski, Manfred and Hilbe, Christian and Semmann, Dirk and Sommerfeld, Ralf and Marotzke, Jochem},
journal = {Nature Communications},
publisher = {Nature Publishing Group},
title = {{Humans choose representatives who enforce cooperation in social dilemmas through extortion}},
doi = {10.1038/ncomms10915},
volume = {7},
year = {2016},
}
@inproceedings{1335,
abstract = {In this paper we review various automata-theoretic formalisms for expressing quantitative properties. We start with finite-state Boolean automata that express the traditional regular properties. We then consider weighted ω-automata that can measure the average density of events, which finite-state Boolean automata cannot. However, even weighted ω-automata cannot express basic performance properties like average response time. We finally consider two formalisms of weighted ω-automata with monitors, where the monitors are either (a) counters or (b) weighted automata themselves. We present a translation result to establish that these two formalisms are equivalent. Weighted ω-automata with monitors generalize weighted ω-automata, and can express average response time property. They present a natural, robust, and expressive framework for quantitative specifications, with important decidable properties.},
author = {Chatterjee, Krishnendu and Henzinger, Thomas A and Otop, Jan},
location = {Edinburgh, United Kingdom},
pages = {23 -- 38},
publisher = {Springer},
title = {{Quantitative monitor automata}},
doi = {10.1007/978-3-662-53413-7_2},
volume = {9837},
year = {2016},
}
@inproceedings{1340,
abstract = {We study repeated games with absorbing states, a type of two-player, zero-sum concurrent mean-payoff games with the prototypical example being the Big Match of Gillete (1957). These games may not allow optimal strategies but they always have ε-optimal strategies. In this paper we design ε-optimal strategies for Player 1 in these games that use only O(log log T) space. Furthermore, we construct strategies for Player 1 that use space s(T), for an arbitrary small unbounded non-decreasing function s, and which guarantee an ε-optimal value for Player 1 in the limit superior sense. The previously known strategies use space Ω(log T) and it was known that no strategy can use constant space if it is ε-optimal even in the limit superior sense. We also give a complementary lower bound. Furthermore, we also show that no Markov strategy, even extended with finite memory, can ensure value greater than 0 in the Big Match, answering a question posed by Neyman [11].},
author = {Hansen, Kristoffer and Ibsen-Jensen, Rasmus and Koucký, Michal},
location = {Liverpool, United Kingdom},
pages = {64 -- 76},
publisher = {Springer},
title = {{The big match in small space}},
doi = {10.1007/978-3-662-53354-3_6},
volume = {9928},
year = {2016},
}
@inproceedings{1389,
abstract = {The continuous evolution of a wide variety of systems, including continous-time Markov chains and linear hybrid automata, can be
described in terms of linear differential equations. In this paper we study the decision problem of whether the solution x(t) of a system of linear differential equations dx/dt = Ax reaches a target halfspace infinitely often. This recurrent reachability problem can
equivalently be formulated as the following Infinite Zeros Problem: does a real-valued function f:R≥0 --> R satisfying a given linear
differential equation have infinitely many zeros? Our main decidability result is that if the differential equation has order at most 7, then the Infinite Zeros Problem is decidable. On the other hand, we show that a decision procedure for the Infinite Zeros Problem at order 9 (and above) would entail a major breakthrough in Diophantine Approximation, specifically an algorithm for computing the Lagrange constants of arbitrary real algebraic numbers to arbitrary precision.},
author = {Chonev, Ventsislav K and Ouaknine, Joël and Worrell, James},
booktitle = {LICS '16},
location = {New York, NY, USA},
pages = {515 -- 524},
publisher = {IEEE},
title = {{On recurrent reachability for continuous linear dynamical systems}},
doi = {10.1145/2933575.2934548},
year = {2016},
}
@phdthesis{1397,
abstract = {We study partially observable Markov decision processes (POMDPs) with objectives used in verification and artificial intelligence. The qualitative analysis problem given a POMDP and an objective asks whether there is a strategy (policy) to ensure that the objective is satisfied almost surely (with probability 1), resp. with positive probability (with probability greater than 0). For POMDPs with limit-average payoff, where a reward value in the interval [0,1] is associated to every transition, and the payoff of an infinite path is the long-run average of the rewards, we consider two types of path constraints: (i) a quantitative limit-average constraint defines the set of paths where the payoff is at least a given threshold L1 = 1. Our main results for qualitative limit-average constraint under almost-sure winning are as follows: (i) the problem of deciding the existence of a finite-memory controller is EXPTIME-complete; and (ii) the problem of deciding the existence of an infinite-memory controller is undecidable. For quantitative limit-average constraints we show that the problem of deciding the existence of a finite-memory controller is undecidable. We present a prototype implementation of our EXPTIME algorithm. For POMDPs with w-regular conditions specified as parity objectives, while the qualitative analysis problems are known to be undecidable even for very special case of parity objectives, we establish decidability (with optimal complexity) of the qualitative analysis problems for POMDPs with parity objectives under finite-memory strategies. We establish optimal (exponential) memory bounds and EXPTIME-completeness of the qualitative analysis problems under finite-memory strategies for POMDPs with parity objectives. Based on our theoretical algorithms we also present a practical approach, where we design heuristics to deal with the exponential complexity, and have applied our implementation on a number of well-known POMDP examples for robotics applications. For POMDPs with a set of target states and an integer cost associated with every transition, we study the optimization objective that asks to minimize the expected total cost of reaching a state in the target set, while ensuring that the target set is reached almost surely. We show that for general integer costs approximating the optimal cost is undecidable. For positive costs, our results are as follows: (i) we establish matching lower and upper bounds for the optimal cost, both double and exponential in the POMDP state space size; (ii) we show that the problem of approximating the optimal cost is decidable and present approximation algorithms that extend existing algorithms for POMDPs with finite-horizon objectives. We show experimentally that it performs well in many examples of interest. We study more deeply the problem of almost-sure reachability, where given a set of target states, the question is to decide whether there is a strategy to ensure that the target set is reached almost surely. While in general the problem EXPTIME-complete, in many practical cases strategies with a small amount of memory suffice. Moreover, the existing solution to the problem is explicit, which first requires to construct explicitly an exponential reduction to a belief-support MDP. We first study the existence of observation-stationary strategies, which is NP-complete, and then small-memory strategies. We present a symbolic algorithm by an efficient encoding to SAT and using a SAT solver for the problem. We report experimental results demonstrating the scalability of our symbolic (SAT-based) approach. Decentralized POMDPs (DEC-POMDPs) extend POMDPs to a multi-agent setting, where several agents operate in an uncertain environment independently to achieve a joint objective. In this work we consider Goal DEC-POMDPs, where given a set of target states, the objective is to ensure that the target set is reached with minimal cost. We consider the indefinite-horizon (infinite-horizon with either discounted-sum, or undiscounted-sum, where absorbing goal states have zero-cost) problem. We present a new and novel method to solve the problem that extends methods for finite-horizon DEC-POMDPs and the real-time dynamic programming approach for POMDPs. We present experimental results on several examples, and show that our approach presents promising results. In the end we present a short summary of a few other results related to verification of MDPs and POMDPs.},
author = {Chmelik, Martin},
pages = {232},
publisher = {IST Austria},
title = {{Algorithms for partially observable markov decision processes}},
year = {2016},
}
@article{1380,
abstract = {We consider higher-dimensional versions of Kannan and Lipton's Orbit Problem - determining whether a target vector space V may be reached from a starting point x under repeated applications of a linear transformation A. Answering two questions posed by Kannan and Lipton in the 1980s, we show that when V has dimension one, this problem is solvable in polynomial time, and when V has dimension two or three, the problem is in NPRP.},
author = {Chonev, Ventsislav K and Ouaknine, Joël and Worrell, James},
journal = {Journal of the ACM},
number = {3},
publisher = {ACM},
title = {{On the complexity of the orbit problem}},
doi = {10.1145/2857050},
volume = {63},
year = {2016},
}
@article{1423,
abstract = {Direct reciprocity is a mechanism for the evolution of cooperation based on repeated interactions. When individuals meet repeatedly, they can use conditional strategies to enforce cooperative outcomes that would not be feasible in one-shot social dilemmas. Direct reciprocity requires that individuals keep track of their past interactions and find the right response. However, there are natural bounds on strategic complexity: Humans find it difficult to remember past interactions accurately, especially over long timespans. Given these limitations, it is natural to ask how complex strategies need to be for cooperation to evolve. Here, we study stochastic evolutionary game dynamics in finite populations to systematically compare the evolutionary performance of reactive strategies, which only respond to the co-player's previous move, and memory-one strategies, which take into account the own and the co-player's previous move. In both cases, we compare deterministic strategy and stochastic strategy spaces. For reactive strategies and small costs, we find that stochasticity benefits cooperation, because it allows for generous-tit-for-tat. For memory one strategies and small costs, we find that stochasticity does not increase the propensity for cooperation, because the deterministic rule of win-stay, lose-shift works best. For memory one strategies and large costs, however, stochasticity can augment cooperation.},
author = {Baek, Seung and Jeong, Hyeongchai and Hilbe, Christian and Nowak, Martin},
journal = {Scientific Reports},
publisher = {Nature Publishing Group},
title = {{Comparing reactive and memory-one strategies of direct reciprocity}},
doi = {10.1038/srep25676},
volume = {6},
year = {2016},
}
@article{1426,
abstract = {Brood parasites exploit their host in order to increase their own fitness. Typically, this results in an arms race between parasite trickery and host defence. Thus, it is puzzling to observe hosts that accept parasitism without any resistance. The ‘mafia’ hypothesis suggests that these hosts accept parasitism to avoid retaliation. Retaliation has been shown to evolve when the hosts condition their response to mafia parasites, who use depredation as a targeted response to rejection. However, it is unclear if acceptance would also emerge when ‘farming’ parasites are present in the population. Farming parasites use depredation to synchronize the timing with the host, destroying mature clutches to force the host to re-nest. Herein, we develop an evolutionary model to analyse the interaction between depredatory parasites and their hosts. We show that coevolutionary cycles between farmers and mafia can still induce host acceptance of brood parasites. However, this equilibrium is unstable and in the long-run the dynamics of this host–parasite interaction exhibits strong oscillations: when farmers are the majority, accepters conditional to mafia (the host will reject first and only accept after retaliation by the parasite) have a higher fitness than unconditional accepters (the host always accepts parasitism). This leads to an increase in mafia parasites’ fitness and in turn induce an optimal environment for accepter hosts.},
author = {Chakra, Maria and Hilbe, Christian and Traulsen, Arne},
journal = {Royal Society Open Science},
number = {5},
publisher = {Royal Society, The},
title = {{Coevolutionary interactions between farmers and mafia induce host acceptance of avian brood parasites}},
doi = {10.1098/rsos.160036},
volume = {3},
year = {2016},
}
@article{1518,
abstract = {The inference of demographic history from genome data is hindered by a lack of efficient computational approaches. In particular, it has proved difficult to exploit the information contained in the distribution of genealogies across the genome. We have previously shown that the generating function (GF) of genealogies can be used to analytically compute likelihoods of demographic models from configurations of mutations in short sequence blocks (Lohse et al. 2011). Although the GF has a simple, recursive form, the size of such likelihood calculations explodes quickly with the number of individuals and applications of this framework have so far been mainly limited to small samples (pairs and triplets) for which the GF can be written by hand. Here we investigate several strategies for exploiting the inherent symmetries of the coalescent. In particular, we show that the GF of genealogies can be decomposed into a set of equivalence classes that allows likelihood calculations from nontrivial samples. Using this strategy, we automated blockwise likelihood calculations for a general set of demographic scenarios in Mathematica. These histories may involve population size changes, continuous migration, discrete divergence, and admixture between multiple populations. To give a concrete example, we calculate the likelihood for a model of isolation with migration (IM), assuming two diploid samples without phase and outgroup information. We demonstrate the new inference scheme with an analysis of two individual butterfly genomes from the sister species Heliconius melpomene rosina and H. cydno.},
author = {Lohse, Konrad and Chmelik, Martin and Martin, Simon and Barton, Nicholas H},
journal = {Genetics},
number = {2},
pages = {775 -- 786},
publisher = {Genetics Society of America},
title = {{Efficient strategies for calculating blockwise likelihoods under the coalescent}},
doi = {10.1534/genetics.115.183814},
volume = {202},
year = {2016},
}
@inproceedings{478,
abstract = {Magic: the Gathering is a game about magical combat for any number of players. Formally it is a zero-sum, imperfect information stochastic game that consists of a potentially unbounded number of steps. We consider the problem of deciding if a move is legal in a given single step of Magic. We show that the problem is (a) coNP-complete in general; and (b) in P if either of two small sets of cards are not used. Our lower bound holds even for single-player Magic games. The significant aspects of our results are as follows: First, in most real-life game problems, the task of deciding whether a given move is legal in a single step is trivial, and the computationally hard task is to find the best sequence of legal moves in the presence of multiple players. In contrast, quite uniquely our hardness result holds for single step and with only one-player. Second, we establish efficient algorithms for important special cases of Magic.},
author = {Chatterjee, Krishnendu and Ibsen-Jensen, Rasmus},
location = {The Hague, Netherlands},
pages = {1432 -- 1439},
publisher = {IOS Press},
title = {{The complexity of deciding legality of a single step of magic: The gathering}},
doi = {10.3233/978-1-61499-672-9-1432},
volume = {285},
year = {2016},
}
@inproceedings{480,
abstract = {Graph games provide the foundation for modeling and synthesizing reactive processes. In the synthesis of stochastic reactive processes, the traditional model is perfect-information stochastic games, where some transitions of the game graph are controlled by two adversarial players, and the other transitions are executed probabilistically. We consider such games where the objective is the conjunction of several quantitative objectives (specified as mean-payoff conditions), which we refer to as generalized mean-payoff objectives. The basic decision problem asks for the existence of a finite-memory strategy for a player that ensures the generalized mean-payoff objective be satisfied with a desired probability against all strategies of the opponent. A special case of the decision problem is the almost-sure problem where the desired probability is 1. Previous results presented a semi-decision procedure for -approximations of the almost-sure problem. In this work, we show that both the almost-sure problem as well as the general basic decision problem are coNP-complete, significantly improving the previous results. Moreover, we show that in the case of 1-player stochastic games, randomized memoryless strategies are sufficient and the problem can be solved in polynomial time. In contrast, in two-player stochastic games, we show that even with randomized strategies exponential memory is required in general, and present a matching exponential upper bound. We also study the basic decision problem with infinite-memory strategies and present computational complexity results for the problem. Our results are relevant in the synthesis of stochastic reactive systems with multiple quantitative requirements.},
author = {Chatterjee, Krishnendu and Doyen, Laurent},
location = {New York, NY, USA},
pages = {247 -- 256},
publisher = {IEEE},
title = {{Perfect-information stochastic games with generalized mean-payoff objectives}},
doi = {10.1145/2933575.2934513},
volume = {05-08-July-2016},
year = {2016},
}
@article{1477,
abstract = {We consider partially observable Markov decision processes (POMDPs) with ω-regular conditions specified as parity objectives. The class of ω-regular languages provides a robust specification language to express properties in verification, and parity objectives are canonical forms to express them. The qualitative analysis problem given a POMDP and a parity objective asks whether there is a strategy to ensure that the objective is satisfied with probability 1 (resp. positive probability). While the qualitative analysis problems are undecidable even for special cases of parity objectives, we establish decidability (with optimal complexity) for POMDPs with all parity objectives under finite-memory strategies. We establish optimal (exponential) memory bounds and EXPTIME-completeness of the qualitative analysis problems under finite-memory strategies for POMDPs with parity objectives. We also present a practical approach, where we design heuristics to deal with the exponential complexity, and have applied our implementation on a number of POMDP examples.},
author = {Chatterjee, Krishnendu and Chmelik, Martin and Tracol, Mathieu},
journal = {Journal of Computer and System Sciences},
number = {5},
pages = {878 -- 911},
publisher = {Elsevier},
title = {{What is decidable about partially observable Markov decision processes with ω-regular objectives}},
doi = {10.1016/j.jcss.2016.02.009},
volume = {82},
year = {2016},
}
@article{1529,
abstract = {We consider partially observable Markov decision processes (POMDPs) with a set of target states and an integer cost associated with every transition. The optimization objective we study asks to minimize the expected total cost of reaching a state in the target set, while ensuring that the target set is reached almost surely (with probability 1). We show that for integer costs approximating the optimal cost is undecidable. For positive costs, our results are as follows: (i) we establish matching lower and upper bounds for the optimal cost, both double exponential in the POMDP state space size; (ii) we show that the problem of approximating the optimal cost is decidable and present approximation algorithms developing on the existing algorithms for POMDPs with finite-horizon objectives. While the worst-case running time of our algorithm is double exponential, we also present efficient stopping criteria for the algorithm and show experimentally that it performs well in many examples of interest.},
author = {Chatterjee, Krishnendu and Chmelik, Martin and Gupta, Raghav and Kanodia, Ayush},
journal = {Artificial Intelligence},
pages = {26 -- 48},
publisher = {Elsevier},
title = {{Optimal cost almost-sure reachability in POMDPs}},
doi = {10.1016/j.artint.2016.01.007},
volume = {234},
year = {2016},
}
@inproceedings{1166,
abstract = {POMDPs are standard models for probabilistic planning problems, where an agent interacts with an uncertain environment. We study the problem of almost-sure reachability, where given a set of target states, the question is to decide whether there is a policy to ensure that the target set is reached with probability 1 (almost-surely). While in general the problem is EXPTIMEcomplete, in many practical cases policies with a small amount of memory suffice. Moreover, the existing solution to the problem is explicit, which first requires to construct explicitly an exponential reduction to a belief-support MDP. In this work, we first study the existence of observation-stationary strategies, which is NP-complete, and then small-memory strategies. We present a symbolic algorithm by an efficient encoding to SAT and using a SAT solver for the problem. We report experimental results demonstrating the scalability of our symbolic (SAT-based) approach. © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.},
author = {Chatterjee, Krishnendu and Chmelik, Martin and Davies, Jessica},
booktitle = {Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence},
location = {Phoenix, AZ, USA},
pages = {3225 -- 3232},
publisher = {AAAI Press},
title = {{A symbolic SAT based algorithm for almost sure reachability with small strategies in pomdps}},
volume = {2016},
year = {2016},
}
@misc{5451,
author = {Pavlogiannis, Andreas and Tkadlec, Josef and Chatterjee, Krishnendu and Nowak, Martin},
issn = {2664-1690},
pages = {34},
publisher = {IST Austria},
title = {{Strong amplifiers of natural selection}},
doi = {10.15479/AT:IST-2016-728-v1-1},
year = {2016},
}
@misc{5445,
abstract = {We consider the quantitative analysis problem for interprocedural control-flow graphs (ICFGs). The input consists of an ICFG, a positive weight function that assigns every transition a positive integer-valued number, and a labelling of the transitions (events) as good, bad, and neutral events. The weight function assigns to each transition a numerical value that represents ameasure of how good or bad an event is. The quantitative analysis problem asks whether there is a run of the ICFG where the ratio of the sum of the numerical weights of good events versus the sum of weights of bad events in the long-run is at least a given threshold (or equivalently, to compute the maximal ratio among all valid paths in the ICFG). The quantitative analysis problem for ICFGs can be solved in polynomial time, and we present an efficient and practical algorithm for the problem. We show that several problems relevant for static program analysis, such as estimating the worst-case execution time of a program or the average energy consumption of a mobile application, can be modeled in our framework. We have implemented our algorithm as a tool in the Java Soot framework. We demonstrate the effectiveness of our approach with two case studies. First, we show that our framework provides a sound approach (no false positives) for the analysis of inefficiently-used containers. Second, we show that our approach can also be used for static profiling of programs which reasons about methods that are frequently invoked. Our experimental results show that our tool scales to relatively large benchmarks, and discovers relevant and useful information that can be used to optimize performance of the programs. },
author = {Chatterjee, Krishnendu and Pavlogiannis, Andreas and Velner, Yaron},
issn = {2664-1690},
pages = {33},
publisher = {IST Austria},
title = {{Quantitative interprocedural analysis}},
doi = {10.15479/AT:IST-2016-523-v1-1},
year = {2016},
}
@misc{5449,
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 Comet-swarm 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 = {2664-1690},
pages = {22},
publisher = {IST Austria},
title = {{Amplification on undirected population structures: Comets beat stars}},
doi = {10.15479/AT:IST-2016-648-v1-1},
year = {2016},
}
@misc{5453,
author = {Pavlogiannis, Andreas and Tkadlec, Josef and Chatterjee, Krishnendu and Nowak, Martin},
issn = {2664-1690},
pages = {34},
publisher = {IST Austria},
title = {{Arbitrarily strong amplifiers of natural selection}},
doi = {10.15479/AT:IST-2017-749-v3-1},
year = {2016},
}
@misc{5452,
author = {Pavlogiannis, Andreas and Tkadlec, Josef and Chatterjee, Krishnendu and Nowak, Martin},
issn = {2664-1690},
pages = {32},
publisher = {IST Austria},
title = {{Arbitrarily strong amplifiers of natural selection}},
doi = {10.15479/AT:IST-2017-728-v2-1},
year = {2016},
}
@inproceedings{1438,
abstract = {In this paper, we consider termination of probabilistic programs with real-valued variables. The questions concerned are: (a) qualitative ones that ask (i) whether the program terminates with probability 1 (almost-sure termination) and (ii) whether the expected termination time is finite (finite termination); (b) quantitative ones that ask (i) to approximate the expected termination time (expectation problem) and (ii) to compute a bound B such that the probability to terminate after B steps decreases exponentially (concentration problem). To solve these questions, we utilize the notion of ranking supermartingales which is a powerful approach for proving termination of probabilistic programs. In detail, we focus on algorithmic synthesis of linear ranking-supermartingales over affine probabilistic programs (APP's) with both angelic and demonic non-determinism. An important subclass of APP's is LRAPP which is defined as the class of all APP's over which a linear ranking-supermartingale exists. Our main contributions are as follows. Firstly, we show that the membership problem of LRAPP (i) can be decided in polynomial time for APP's with at most demonic non-determinism, and (ii) is NP-hard and in PSPACE for APP's with angelic non-determinism; moreover, the NP-hardness result holds already for APP's without probability and demonic non-determinism. Secondly, we show that the concentration problem over LRAPP can be solved in the same complexity as for the membership problem of LRAPP. Finally, we show that the expectation problem over LRAPP can be solved in 2EXPTIME and is PSPACE-hard even for APP's without probability and non-determinism (i.e., deterministic programs). Our experimental results demonstrate the effectiveness of our approach to answer the qualitative and quantitative questions over APP's with at most demonic non-determinism.},
author = {Chatterjee, Krishnendu and Fu, Hongfei and Novotny, Petr and Hasheminezhad, Rouzbeh},
location = {St. Petersburg, FL, USA},
pages = {327 -- 342},
publisher = {ACM},
title = {{Algorithmic analysis of qualitative and quantitative termination problems for affine probabilistic programs}},
doi = {10.1145/2837614.2837639},
volume = {20-22},
year = {2016},
}
@inproceedings{1071,
abstract = {We consider data-structures for answering reachability and distance queries on constant-treewidth graphs with n nodes, on the standard RAM computational model with wordsize W=Theta(log n). Our first contribution is a data-structure that after O(n) preprocessing time, allows (1) pair reachability queries in O(1) time; and (2) single-source reachability queries in O(n/log n) time. This is (asymptotically) optimal and is faster than DFS/BFS when answering more than a constant number of single-source queries. The data-structure uses at all times O(n) space. Our second contribution is a space-time tradeoff data-structure for distance queries. For any epsilon in [1/2,1], we provide a data-structure with polynomial preprocessing time that allows pair queries in O(n^{1-\epsilon} alpha(n)) time, where alpha is the inverse of the Ackermann function, and at all times uses O(n^epsilon) space. The input graph G is not considered in the space complexity. },
author = {Chatterjee, Krishnendu and Ibsen-Jensen, Rasmus and Pavlogiannis, Andreas},
location = {Aarhus, Denmark},
publisher = {Schloss Dagstuhl- Leibniz-Zentrum fur Informatik},
title = {{Optimal reachability and a space time tradeoff for distance queries in constant treewidth graphs}},
doi = {10.4230/LIPIcs.ESA.2016.28},
volume = {57},
year = {2016},
}
@inproceedings{1437,
abstract = {We study algorithmic questions for concurrent systems where the transitions are labeled from a complete, closed semiring, and path properties are algebraic with semiring operations. The algebraic path properties can model dataflow analysis problems, the shortest path problem, and many other natural problems that arise in program analysis. We consider that each component of the concurrent system is a graph with constant treewidth, a property satisfied by the controlflow graphs of most programs. We allow for multiple possible queries, which arise naturally in demand driven dataflow analysis. The study of multiple queries allows us to consider the tradeoff between the resource usage of the one-time preprocessing and for each individual query. The traditional approach constructs the product graph of all components and applies the best-known graph algorithm on the product. In this approach, even the answer to a single query requires the transitive closure (i.e., the results of all possible queries), which provides no room for tradeoff between preprocessing and query time. Our main contributions are algorithms that significantly improve the worst-case running time of the traditional approach, and provide various tradeoffs depending on the number of queries. For example, in a concurrent system of two components, the traditional approach requires hexic time in the worst case for answering one query as well as computing the transitive closure, whereas we show that with one-time preprocessing in almost cubic time, each subsequent query can be answered in at most linear time, and even the transitive closure can be computed in almost quartic time. Furthermore, we establish conditional optimality results showing that the worst-case running time of our algorithms cannot be improved without achieving major breakthroughs in graph algorithms (i.e., improving the worst-case bound for the shortest path problem in general graphs). Preliminary experimental results show that our algorithms perform favorably on several benchmarks.},
author = {Chatterjee, Krishnendu and Goharshady, Amir and Ibsen-Jensen, Rasmus and Pavlogiannis, Andreas},
location = {St. Petersburg, FL, USA},
pages = {733 -- 747},
publisher = {ACM},
title = {{Algorithms for algebraic path properties in concurrent systems of constant treewidth components}},
doi = {10.1145/2837614.2837624},
volume = {20-22},
year = {2016},
}
@inproceedings{1386,
abstract = {We consider nondeterministic probabilistic programs with the most basic liveness property of termination. We present efficient methods for termination analysis of nondeterministic probabilistic programs with polynomial guards and assignments. Our approach is through synthesis of polynomial ranking supermartingales, that on one hand significantly generalizes linear ranking supermartingales and on the other hand is a counterpart of polynomial ranking-functions for proving termination of nonprobabilistic programs. The approach synthesizes polynomial ranking-supermartingales through Positivstellensatz's, yielding an efficient method which is not only sound, but also semi-complete over a large subclass of programs. We show experimental results to demonstrate that our approach can handle several classical programs with complex polynomial guards and assignments, and can synthesize efficient quadratic ranking-supermartingales when a linear one does not exist even for simple affine programs.},
author = {Chatterjee, Krishnendu and Fu, Hongfei and Goharshady, Amir},
location = {Toronto, Canada},
pages = {3 -- 22},
publisher = {Springer},
title = {{Termination analysis of probabilistic programs through Positivstellensatz's}},
doi = {10.1007/978-3-319-41528-4_1},
volume = {9779},
year = {2016},
}
@inproceedings{1499,
abstract = {We consider weighted automata with both positive and negative integer weights on edges and
study the problem of synchronization using adaptive strategies that may only observe whether
the current weight-level is negative or nonnegative. We show that the synchronization problem is decidable in polynomial time for deterministic weighted automata.},
author = {Kretinsky, Jan and Larsen, Kim and Laursen, Simon and Srba, Jiří},
location = {Madrid, Spain},
pages = {142 -- 154},
publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik},
title = {{Polynomial time decidability of weighted synchronization under partial observability}},
doi = {10.4230/LIPIcs.CONCUR.2015.142},
volume = {42},
year = {2015},
}
@article{1559,
abstract = {There are deep, yet largely unexplored, connections between computer science and biology. Both disciplines examine how information proliferates in time and space. Central results in computer science describe the complexity of algorithms that solve certain classes of problems. An algorithm is deemed efficient if it can solve a problem in polynomial time, which means the running time of the algorithm is a polynomial function of the length of the input. There are classes of harder problems for which the fastest possible algorithm requires exponential time. Another criterion is the space requirement of the algorithm. There is a crucial distinction between algorithms that can find a solution, verify a solution, or list several distinct solutions in given time and space. The complexity hierarchy that is generated in this way is the foundation of theoretical computer science. Precise complexity results can be notoriously difficult. The famous question whether polynomial time equals nondeterministic polynomial time (i.e., P = NP) is one of the hardest open problems in computer science and all of mathematics. Here, we consider simple processes of ecological and evolutionary spatial dynamics. The basic question is: What is the probability that a new invader (or a new mutant)will take over a resident population?We derive precise complexity results for a variety of scenarios. We therefore show that some fundamental questions in this area cannot be answered by simple equations (assuming that P is not equal to NP).},
author = {Ibsen-Jensen, Rasmus and Chatterjee, Krishnendu and Nowak, Martin},
journal = {PNAS},
number = {51},
pages = {15636 -- 15641},
publisher = {National Academy of Sciences},
title = {{Computational complexity of ecological and evolutionary spatial dynamics}},
doi = {10.1073/pnas.1511366112},
volume = {112},
year = {2015},
}
@inproceedings{1502,
abstract = {We extend the theory of input-output conformance with operators for merge and quotient. The former is useful when testing against multiple requirements or views. The latter can be used to generate tests for patches of an already tested system. Both operators can combine systems with different action alphabets, which is usually the case when constructing complex systems and specifications from parts, for instance different views as well as newly defined functionality of a~previous version of the system.},
author = {Beneš, Nikola and Daca, Przemyslaw and Henzinger, Thomas A and Kretinsky, Jan and Nickovic, Dejan},
isbn = {978-1-4503-3471-6},
location = {Montreal, QC, Canada},
pages = {101 -- 110},
publisher = {ACM},
title = {{Complete composition operators for IOCO-testing theory}},
doi = {10.1145/2737166.2737175},
year = {2015},
}
@article{1501,
abstract = {We consider Markov decision processes (MDPs) which are a standard model for probabilistic systems. We focus on qualitative properties for MDPs that can express that desired behaviors of the system arise almost-surely (with probability 1) or with positive probability. We introduce a new simulation relation to capture the refinement relation of MDPs with respect to qualitative properties, and present discrete graph algorithms with quadratic complexity to compute the simulation relation. We present an automated technique for assume-guarantee style reasoning for compositional analysis of two-player games by giving a counterexample guided abstraction-refinement approach to compute our new simulation relation. We show a tight link between two-player games and MDPs, and as a consequence the results for games are lifted to MDPs with qualitative properties. We have implemented our algorithms and show that the compositional analysis leads to significant improvements. },
author = {Chatterjee, Krishnendu and Chmelik, Martin and Daca, Przemyslaw},
journal = {Formal Methods in System Design},
number = {2},
pages = {230 -- 264},
publisher = {Springer},
title = {{CEGAR for compositional analysis of qualitative properties in Markov decision processes}},
doi = {10.1007/s10703-015-0235-2},
volume = {47},
year = {2015},
}
@inproceedings{1594,
abstract = {Quantitative extensions of temporal logics have recently attracted significant attention. In this work, we study frequency LTL (fLTL), an extension of LTL which allows to speak about frequencies of events along an execution. Such an extension is particularly useful for probabilistic systems that often cannot fulfil strict qualitative guarantees on the behaviour. It has been recently shown that controller synthesis for Markov decision processes and fLTL is decidable when all the bounds on frequencies are 1. As a step towards a complete quantitative solution, we show that the problem is decidable for the fragment fLTL\GU, where U does not occur in the scope of G (but still F can). Our solution is based on a novel translation of such quantitative formulae into equivalent deterministic automata.},
author = {Forejt, Vojtěch and Krčál, Jan and Kretinsky, Jan},
location = {Suva, Fiji},
pages = {162 -- 177},
publisher = {Springer},
title = {{Controller synthesis for MDPs and frequency LTL\GU}},
doi = {10.1007/978-3-662-48899-7_12},
volume = {9450},
year = {2015},
}
@inproceedings{1601,
abstract = {We propose a flexible exchange format for ω-automata, as typically used in formal verification, and implement support for it in a range of established tools. Our aim is to simplify the interaction of tools, helping the research community to build upon other people’s work. A key feature of the format is the use of very generic acceptance conditions, specified by Boolean combinations of acceptance primitives, rather than being limited to common cases such as Büchi, Streett, or Rabin. Such flexibility in the choice of acceptance conditions can be exploited in applications, for example in probabilistic model checking, and furthermore encourages the development of acceptance-agnostic tools for automata manipulations. The format allows acceptance conditions that are either state-based or transition-based, and also supports alternating automata.},
author = {Babiak, Tomáš and Blahoudek, František and Duret Lutz, Alexandre and Klein, Joachim and Kretinsky, Jan and Mueller, Daniel and Parker, David and Strejček, Jan},
location = {San Francisco, CA, United States},
pages = {479 -- 486},
publisher = {Springer},
title = {{The Hanoi omega-automata format}},
doi = {10.1007/978-3-319-21690-4_31},
volume = {9206},
year = {2015},
}
@inproceedings{1609,
abstract = {The synthesis problem asks for the automatic construction of a system from its specification. In the traditional setting, the system is “constructed from scratch” rather than composed from reusable components. However, this is rare in practice, and almost every non-trivial software system relies heavily on the use of libraries of reusable components. Recently, Lustig and Vardi introduced dataflow and controlflow synthesis from libraries of reusable components. They proved that dataflow synthesis is undecidable, while controlflow synthesis is decidable. The problem of controlflow synthesis from libraries of probabilistic components was considered by Nain, Lustig and Vardi, and was shown to be decidable for qualitative analysis (that asks that the specification be satisfied with probability 1). Our main contribution for controlflow synthesis from probabilistic components is to establish better complexity bounds for the qualitative analysis problem, and to show that the more general quantitative problem is undecidable. For the qualitative analysis, we show that the problem (i) is EXPTIME-complete when the specification is given as a deterministic parity word automaton, improving the previously known 2EXPTIME upper bound; and (ii) belongs to UP ∩ coUP and is parity-games hard, when the specification is given directly as a parity condition on the components, improving the previously known EXPTIME upper bound.},
author = {Chatterjee, Krishnendu and Doyen, Laurent and Vardi, Moshe},
location = {Kyoto, Japan},
pages = {108 -- 120},
publisher = {Springer},
title = {{The complexity of synthesis from probabilistic components}},
doi = {10.1007/978-3-662-47666-6_9},
volume = {9135},
year = {2015},
}
@article{1624,
abstract = {Population structure can facilitate evolution of cooperation. In a structured population, cooperators can form clusters which resist exploitation by defectors. Recently, it was observed that a shift update rule is an extremely strong amplifier of cooperation in a one dimensional spatial model. For the shift update rule, an individual is chosen for reproduction proportional to fecundity; the offspring is placed next to the parent; a random individual dies. Subsequently, the population is rearranged (shifted) until all individual cells are again evenly spaced out. For large population size and a one dimensional population structure, the shift update rule favors cooperation for any benefit-to-cost ratio greater than one. But every attempt to generalize shift updating to higher dimensions while maintaining its strong effect has failed. The reason is that in two dimensions the clusters are fragmented by the movements caused by rearranging the cells. Here we introduce the natural phenomenon of a repulsive force between cells of different types. After a birth and death event, the cells are being rearranged minimizing the overall energy expenditure. If the repulsive force is sufficiently high, shift becomes a strong promoter of cooperation in two dimensions.},
author = {Pavlogiannis, Andreas and Chatterjee, Krishnendu and Adlam, Ben and Nowak, Martin},
journal = {Scientific Reports},
publisher = {Nature Publishing Group},
title = {{Cellular cooperation with shift updating and repulsion}},
doi = {10.1038/srep17147},
volume = {5},
year = {2015},
}
@inproceedings{1660,
abstract = {We study the pattern frequency vector for runs in probabilistic Vector Addition Systems with States (pVASS). Intuitively, each configuration of a given pVASS is assigned one of finitely many patterns, and every run can thus be seen as an infinite sequence of these patterns. The pattern frequency vector assigns to each run the limit of pattern frequencies computed for longer and longer prefixes of the run. If the limit does not exist, then the vector is undefined. We show that for one-counter pVASS, the pattern frequency vector is defined and takes one of finitely many values for almost all runs. Further, these values and their associated probabilities can be approximated up to an arbitrarily small relative error in polynomial time. For stable two-counter pVASS, we show the same result, but we do not provide any upper complexity bound. As a byproduct of our study, we discover counterexamples falsifying some classical results about stochastic Petri nets published in the 80s.},
author = {Brázdil, Tomáš and Kiefer, Stefan and Kučera, Antonín and Novotny, Petr},
location = {Kyoto, Japan},
pages = {44 -- 55},
publisher = {IEEE},
title = {{Long-run average behaviour of probabilistic vector addition systems}},
doi = {10.1109/LICS.2015.15},
year = {2015},
}
@inproceedings{1667,
abstract = {We consider parametric version of fixed-delay continuoustime Markov chains (or equivalently deterministic and stochastic Petri nets, DSPN) where fixed-delay transitions are specified by parameters, rather than concrete values. Our goal is to synthesize values of these parameters that, for a given cost function, minimise expected total cost incurred before reaching a given set of target states. We show that under mild assumptions, optimal values of parameters can be effectively approximated using translation to a Markov decision process (MDP) whose actions correspond to discretized values of these parameters. To this end we identify and overcome several interesting phenomena arising in systems with fixed delays.},
author = {Brázdil, Tomáš and Korenčiak, L'Uboš and Krčál, Jan and Novotny, Petr and Řehák, Vojtěch},
location = {Madrid, Spain},
pages = {141 -- 159},
publisher = {Springer},
title = {{Optimizing performance of continuous-time stochastic systems using timeout synthesis}},
doi = {10.1007/978-3-319-22264-6_10},
volume = {9259},
year = {2015},
}
@article{1673,
abstract = {When a new mutant arises in a population, there is a probability it outcompetes the residents and fixes. The structure of the population can affect this fixation probability. Suppressing population structures reduce the difference between two competing variants, while amplifying population structures enhance the difference. Suppressors are ubiquitous and easy to construct, but amplifiers for the large population limit are more elusive and only a few examples have been discovered. Whether or not a population structure is an amplifier of selection depends on the probability distribution for the placement of the invading mutant. First, we prove that there exist only bounded amplifiers for adversarial placement-that is, for arbitrary initial conditions. Next, we show that the Star population structure, which is known to amplify for mutants placed uniformly at random, does not amplify for mutants that arise through reproduction and are therefore placed proportional to the temperatures of the vertices. Finally, we construct population structures that amplify for all mutational events that arise through reproduction, uniformly at random, or through some combination of the two. },
author = {Adlam, Ben and Chatterjee, Krishnendu and Nowak, Martin},
journal = {Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences},
number = {2181},
publisher = {Royal Society of London},
title = {{Amplifiers of selection}},
doi = {10.1098/rspa.2015.0114},
volume = {471},
year = {2015},
}
@article{1665,
abstract = {Which genetic alterations drive tumorigenesis and how they evolve over the course of disease and therapy are central questions in cancer biology. Here we identify 44 recurrently mutated genes and 11 recurrent somatic copy number variations through whole-exome sequencing of 538 chronic lymphocytic leukaemia (CLL) and matched germline DNA samples, 278 of which were collected in a prospective clinical trial. These include previously unrecognized putative cancer drivers (RPS15, IKZF3), and collectively identify RNA processing and export, MYC activity, and MAPK signalling as central pathways involved in CLL. Clonality analysis of this large data set further enabled reconstruction of temporal relationships between driver events. Direct comparison between matched pre-treatment and relapse samples from 59 patients demonstrated highly frequent clonal evolution. Thus, large sequencing data sets of clinically informative samples enable the discovery of novel genes associated with cancer, the network of relationships between the driver events, and their impact on disease relapse and clinical outcome.},
author = {Landau, Dan and Tausch, Eugen and Taylor Weiner, Amaro and Stewart, Chip and Reiter, Johannes and Bahlo, Jasmin and Kluth, Sandra and Božić, Ivana and Lawrence, Michael and Böttcher, Sebastian and Carter, Scott and Cibulskis, Kristian and Mertens, Daniel and Sougnez, Carrie and Rosenberg, Mara and Hess, Julian and Edelmann, Jennifer and Kless, Sabrina and Kneba, Michael and Ritgen, Matthias and Fink, Anna and Fischer, Kirsten and Gabriel, Stacey and Lander, Eric and Nowak, Martin and Döhner, Hartmut and Hallek, Michael and Neuberg, Donna and Getz, Gad and Stilgenbauer, Stephan and Wu, Catherine},
journal = {Nature},
number = {7574},
pages = {525 -- 530},
publisher = {Nature Publishing Group},
title = {{Mutations driving CLL and their evolution in progression and relapse}},
doi = {10.1038/nature15395},
volume = {526},
year = {2015},
}
@inproceedings{1689,
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 satisfying initial states. Moreover, for any (partial) solution our algorithm synthesizes witness control strategies to ensure almost-sure satisfaction of the temporal logic specification. We demonstrate our approach on an illustrative case study.},
author = {Svoreňová, Mária and Kretinsky, Jan and Chmelik, Martin and Chatterjee, Krishnendu and Cěrná, Ivana and Belta, Cǎlin},
booktitle = {Proceedings of the 18th International Conference on Hybrid Systems: Computation and Control},
location = {Seattle, WA, United States},
pages = {259 -- 268},
publisher = {ACM},
title = {{Temporal logic control for stochastic linear systems using abstraction refinement of probabilistic games}},
doi = {10.1145/2728606.2728608},
year = {2015},
}
@inproceedings{1691,
abstract = {We consider a case study of the problem of deploying an autonomous air vehicle in a partially observable, dynamic, indoor environment from a specification given as a linear temporal logic (LTL) formula over regions of interest. We model the motion and sensing capabilities of the vehicle as a partially observable Markov decision process (POMDP). We adapt recent results for solving POMDPs with parity objectives to generate a control policy. We also extend the existing framework with a policy minimization technique to obtain a better implementable policy, while preserving its correctness. The proposed techniques are illustrated in an experimental setup involving an autonomous quadrotor performing surveillance in a dynamic environment.},
author = {Svoreňová, Mária and Chmelik, Martin and Leahy, Kevin and Eniser, Hasan and Chatterjee, Krishnendu and Cěrná, Ivana and Belta, Cǎlin},
booktitle = {Proceedings of the 18th International Conference on Hybrid Systems: Computation and Control},
location = {Seattle, WA, United States},
pages = {233 -- 238},
publisher = {ACM},
title = {{Temporal logic motion planning using POMDPs with parity objectives: Case study paper}},
doi = {10.1145/2728606.2728617},
year = {2015},
}
@article{1694,
abstract = {
We introduce quantitative timed refinement and timed simulation (directed) metrics, incorporating zenoness checks, for timed systems. These metrics assign positive real numbers which quantify the timing mismatches between two timed systems, amongst non-zeno runs. We quantify timing mismatches in three ways: (1) the maximal timing mismatch that can arise, (2) the “steady-state” maximal timing mismatches, where initial transient timing mismatches are ignored; and (3) the (long-run) average timing mismatches amongst two systems. These three kinds of mismatches constitute three important types of timing differences. Our event times are the global times, measured from the start of the system execution, not just the time durations of individual steps. We present algorithms over timed automata for computing the three quantitative simulation distances to within any desired degree of accuracy. In order to compute the values of the quantitative simulation distances, we use a game theoretic formulation. We introduce two new kinds of objectives for two player games on finite-state game graphs: (1) eventual debit-sum level objectives, and (2) average debit-sum level objectives. We present algorithms for computing the optimal values for these objectives in graph games, and then use these algorithms to compute the values of the timed simulation distances over timed automata.
},
author = {Chatterjee, Krishnendu and Prabhu, Vinayak},
journal = {IEEE Transactions on Automatic Control},
number = {9},
pages = {2291 -- 2306},
publisher = {IEEE},
title = {{Quantitative temporal simulation and refinement distances for timed systems}},
doi = {10.1109/TAC.2015.2404612},
volume = {60},
year = {2015},
}
@article{1698,
abstract = {In mean-payoff games, the objective of the protagonist is to ensure that the limit average of an infinite sequence of numeric weights is nonnegative. In energy games, the objective is to ensure that the running sum of weights is always nonnegative. Multi-mean-payoff and multi-energy games replace individual weights by tuples, and the limit average (resp., running sum) of each coordinate must be (resp., remain) nonnegative. We prove finite-memory determinacy of multi-energy games and show inter-reducibility of multi-mean-payoff and multi-energy games for finite-memory strategies. We improve the computational complexity for solving both classes with finite-memory strategies: we prove coNP-completeness improving the previous known EXPSPACE bound. For memoryless strategies, we show that deciding the existence of a winning strategy for the protagonist is NP-complete. We present the first solution of multi-mean-payoff games with infinite-memory strategies: we show that mean-payoff-sup objectives can be decided in NP∩coNP, whereas mean-payoff-inf objectives are coNP-complete.},
author = {Velner, Yaron and Chatterjee, Krishnendu and Doyen, Laurent and Henzinger, Thomas A and Rabinovich, Alexander and Raskin, Jean},
journal = {Information and Computation},
number = {4},
pages = {177 -- 196},
publisher = {Elsevier},
title = {{The complexity of multi-mean-payoff and multi-energy games}},
doi = {10.1016/j.ic.2015.03.001},
volume = {241},
year = {2015},
}
@article{1681,
abstract = {In many social situations, individuals endeavor to find the single best possible partner, but are constrained to evaluate the candidates in sequence. Examples include the search for mates, economic partnerships, or any other long-term ties where the choice to interact involves two parties. Surprisingly, however, previous theoretical work on mutual choice problems focuses on finding equilibrium solutions, while ignoring the evolutionary dynamics of decisions. Empirically, this may be of high importance, as some equilibrium solutions can never be reached unless the population undergoes radical changes and a sufficient number of individuals change their decisions simultaneously. To address this question, we apply a mutual choice sequential search problem in an evolutionary game-theoretical model that allows one to find solutions that are favored by evolution. As an example, we study the influence of sequential search on the evolutionary dynamics of cooperation. For this, we focus on the classic snowdrift game and the prisoner’s dilemma game.},
author = {Priklopil, Tadeas and Chatterjee, Krishnendu},
journal = {Games},
number = {4},
pages = {413 -- 437},
publisher = {Multidisciplinary Digital Publishing Institute},
title = {{Evolution of decisions in population games with sequentially searching individuals}},
doi = {10.3390/g6040413},
volume = {6},
year = {2015},
}