@inproceedings{645,
abstract = {Markov decision processes (MDPs) are standard models for probabilistic systems with non-deterministic behaviours. Long-run average rewards provide a mathematically elegant formalism for expressing long term performance. Value iteration (VI) is one of the simplest and most efficient algorithmic approaches to MDPs with other properties, such as reachability objectives. Unfortunately, a naive extension of VI does not work for MDPs with long-run average rewards, as there is no known stopping criterion. In this work our contributions are threefold. (1) We refute a conjecture related to stopping criteria for MDPs with long-run average rewards. (2) We present two practical algorithms for MDPs with long-run average rewards based on VI. First, we show that a combination of applying VI locally for each maximal end-component (MEC) and VI for reachability objectives can provide approximation guarantees. Second, extending the above approach with a simulation-guided on-demand variant of VI, we present an anytime algorithm that is able to deal with very large models. (3) Finally, we present experimental results showing that our methods significantly outperform the standard approaches on several benchmarks.},
author = {Ashok, Pranav and Chatterjee, Krishnendu and Daca, Przemyslaw and Kretinsky, Jan and Meggendorfer, Tobias},
editor = {Majumdar, Rupak and Kunčak, Viktor},
isbn = {978-331963386-2},
location = {Heidelberg, Germany},
pages = {201 -- 221},
publisher = {Springer},
title = {{Value iteration for long run average reward in markov decision processes}},
doi = {10.1007/978-3-319-63387-9_10},
volume = {10426},
year = {2017},
}
@inproceedings{6519,
abstract = {Graph games with omega-regular winning conditions provide a mathematical framework to analyze a wide range of problems in the analysis of reactive systems and programs (such as the synthesis of reactive systems, program repair, and the verification of branching time properties). Parity conditions are canonical forms to specify omega-regular winning conditions. Graph games with parity conditions are equivalent to mu-calculus model checking, and thus a very important algorithmic problem. Symbolic algorithms are of great significance because they provide scalable algorithms for the analysis of large finite-state systems, as well as algorithms for the analysis of infinite-state systems with finite quotient. A set-based symbolic algorithm uses the basic set operations and the one-step predecessor operators. We consider graph games with n vertices and parity conditions with c priorities (equivalently, a mu-calculus formula with c alternations of least and greatest fixed points). While many explicit algorithms exist for graph games with parity conditions, for set-based symbolic algorithms there are only two algorithms (notice that we use space to refer to the number of sets stored by a symbolic algorithm): (a) the basic algorithm that requires O(n^c) symbolic operations and linear space; and (b) an improved algorithm that requires O(n^{c/2+1}) symbolic operations but also O(n^{c/2+1}) space (i.e., exponential space). In this work we present two set-based symbolic algorithms for parity games: (a) our first algorithm requires O(n^{c/2+1}) symbolic operations and only requires linear space; and (b) developing on our first algorithm, we present an algorithm that requires O(n^{c/3+1}) symbolic operations and only linear space. We also present the first linear space set-based symbolic algorithm for parity games that requires at most a sub-exponential number of symbolic operations. },
author = {Chatterjee, Krishnendu and Dvorák, Wolfgang and Henzinger, Monika and Loitzenbauer, Veronika},
location = {Stockholm, Sweden},
publisher = {Schloss Dagstuhl -Leibniz-Zentrum fuer Informatik},
title = {{Improved set-based symbolic algorithms for parity games}},
doi = {10.4230/LIPICS.CSL.2017.18},
volume = {82},
year = {2017},
}
@article{653,
abstract = {The extent of heterogeneity among driver gene mutations present in naturally occurring metastases - that is, treatment-naive metastatic disease - is largely unknown. To address this issue, we carried out 60× whole-genome sequencing of 26 metastases from four patients with pancreatic cancer. We found that identical mutations in known driver genes were present in every metastatic lesion for each patient studied. Passenger gene mutations, which do not have known or predicted functional consequences, accounted for all intratumoral heterogeneity. Even with respect to these passenger mutations, our analysis suggests that the genetic similarity among the founding cells of metastases was higher than that expected for any two cells randomly taken from a normal tissue. The uniformity of known driver gene mutations among metastases in the same patient has critical and encouraging implications for the success of future targeted therapies in advanced-stage disease.},
author = {Makohon Moore, Alvin and Zhang, Ming and Reiter, Johannes and Božić, Ivana and Allen, Benjamin and Kundu, Deepanjan and Chatterjee, Krishnendu and Wong, Fay and Jiao, Yuchen and Kohutek, Zachary and Hong, Jungeui and Attiyeh, Marc and Javier, Breanna and Wood, Laura and Hruban, Ralph and Nowak, Martin and Papadopoulos, Nickolas and Kinzler, Kenneth and Vogelstein, Bert and Iacobuzio Donahue, Christine},
issn = {10614036},
journal = {Nature Genetics},
number = {3},
pages = {358 -- 366},
publisher = {Nature Publishing Group},
title = {{Limited heterogeneity of known driver gene mutations among the metastases of individual patients with pancreatic cancer}},
doi = {10.1038/ng.3764},
volume = {49},
year = {2017},
}
@article{671,
abstract = {Humans routinely use conditionally cooperative strategies when interacting in repeated social dilemmas. They are more likely to cooperate if others cooperated before, and are ready to retaliate if others defected. To capture the emergence of reciprocity, most previous models consider subjects who can only choose from a restricted set of representative strategies, or who react to the outcome of the very last round only. As players memorize more rounds, the dimension of the strategy space increases exponentially. This increasing computational complexity renders simulations for individuals with higher cognitive abilities infeasible, especially if multiplayer interactions are taken into account. Here, we take an axiomatic approach instead. We propose several properties that a robust cooperative strategy for a repeated multiplayer dilemma should have. These properties naturally lead to a unique class of cooperative strategies, which contains the classical Win-Stay Lose-Shift rule as a special case. A comprehensive numerical analysis for the prisoner's dilemma and for the public goods game suggests that strategies of this class readily evolve across various memory-n spaces. Our results reveal that successful strategies depend not only on how cooperative others were in the past but also on the respective context of cooperation.},
author = {Hilbe, Christian and Martinez, Vaquero and Chatterjee, Krishnendu and Nowak, Martin},
issn = {00278424},
journal = {PNAS},
number = {18},
pages = {4715 -- 4720},
publisher = {National Academy of Sciences},
title = {{Memory-n strategies of direct reciprocity}},
doi = {10.1073/pnas.1621239114},
volume = {114},
year = {2017},
}
@article{681,
abstract = {Two-player games on graphs provide the theoretical framework for many important problems such as reactive synthesis. While the traditional study of two-player zero-sum games has been extended to multi-player games with several notions of equilibria, they are decidable only for perfect-information games, whereas several applications require imperfect-information. In this paper we propose a new notion of equilibria, called doomsday equilibria, which is a strategy profile where all players satisfy their own objective, and if any coalition of players deviates and violates even one of the players' objective, then the objective of every player is violated. We present algorithms and complexity results for deciding the existence of doomsday equilibria for various classes of ω-regular objectives, both for imperfect-information games, and for perfect-information games. We provide optimal complexity bounds for imperfect-information games, and in most cases for perfect-information games.},
author = {Chatterjee, Krishnendu and Doyen, Laurent and Filiot, Emmanuel and Raskin, Jean},
issn = {08905401},
journal = {Information and Computation},
pages = {296 -- 315},
publisher = {Elsevier},
title = {{Doomsday equilibria for omega-regular games}},
doi = {10.1016/j.ic.2016.10.012},
volume = {254},
year = {2017},
}
@article{684,
abstract = {We generalize winning conditions in two-player games by adding a structural acceptance condition called obligations. Obligations are orthogonal to the linear winning conditions that define whether a play is winning. Obligations are a declaration that player 0 can achieve a certain value from a configuration. If the obligation is met, the value of that configuration for player 0 is 1. We define the value in such games and show that obligation games are determined. For Markov chains with Borel objectives and obligations, and finite turn-based stochastic parity games with obligations we give an alternative and simpler characterization of the value function. Based on this simpler definition we show that the decision problem of winning finite turn-based stochastic parity games with obligations is in NP∩co-NP. We also show that obligation games provide a game framework for reasoning about p-automata. © 2017 The Association for Symbolic Logic.},
author = {Chatterjee, Krishnendu and Piterman, Nir},
issn = {Obligation blackwell games and p-automata},
journal = {Journal of Symbolic Logic},
number = {2},
pages = {420 -- 452},
publisher = {Cambridge University Press},
title = {{Obligation blackwell games and p-automata}},
doi = {10.1017/jsl.2016.71},
volume = {82},
year = {2017},
}
@article{699,
abstract = {In antagonistic symbioses, such as host–parasite interactions, one population’s success is the other’s loss. In mutualistic symbioses, such as division of labor, both parties can gain, but they might have different preferences over the possible mutualistic arrangements. The rates of evolution of the two populations in a symbiosis are important determinants of which population will be more successful: Faster evolution is thought to be favored in antagonistic symbioses (the “Red Queen effect”), but disfavored in certain mutualistic symbioses (the “Red King effect”). However, it remains unclear which biological parameters drive these effects. Here, we analyze the effects of the various determinants of evolutionary rate: generation time, mutation rate, population size, and the intensity of natural selection. Our main results hold for the case where mutation is infrequent. Slower evolution causes a long-term advantage in an important class of mutualistic interactions. Surprisingly, less intense selection is the strongest driver of this Red King effect, whereas relative mutation rates and generation times have little effect. In antagonistic interactions, faster evolution by any means is beneficial. Our results provide insight into the demographic evolution of symbionts. },
author = {Veller, Carl and Hayward, Laura and Nowak, Martin and Hilbe, Christian},
issn = {00278424},
journal = {PNAS},
number = {27},
pages = {E5396 -- E5405},
publisher = {National Academy of Sciences},
title = {{The red queen and king in finite populations}},
doi = {10.1073/pnas.1702020114},
volume = {114},
year = {2017},
}
@inproceedings{711,
abstract = {Nested weighted automata (NWA) present a robust and convenient automata-theoretic formalism for quantitative specifications. Previous works have considered NWA that processed input words only in the forward direction. It is natural to allow the automata to process input words backwards as well, for example, to measure the maximal or average time between a response and the preceding request. We therefore introduce and study bidirectional NWA that can process input words in both directions. First, we show that bidirectional NWA can express interesting quantitative properties that are not expressible by forward-only NWA. Second, for the fundamental decision problems of emptiness and universality, we establish decidability and complexity results for the new framework which match the best-known results for the special case of forward-only NWA. Thus, for NWA, the increased expressiveness of bidirectionality is achieved at no additional computational complexity. This is in stark contrast to the unweighted case, where bidirectional finite automata are no more expressive but exponentially more succinct than their forward-only counterparts.},
author = {Chatterjee, Krishnendu and Henzinger, Thomas A and Otop, Jan},
issn = {18688969},
location = {Berlin, Germany},
publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik},
title = {{Bidirectional nested weighted automata}},
doi = {10.4230/LIPIcs.CONCUR.2017.5},
volume = {85},
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},
}
@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},
}
@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 = {03029743},
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{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 = {00200190},
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},
}
@article{1080,
abstract = {Reconstructing the evolutionary history of metastases is critical for understanding their basic biological principles and has profound clinical implications. Genome-wide sequencing data has enabled modern phylogenomic methods to accurately dissect subclones and their phylogenies from noisy and impure bulk tumour samples at unprecedented depth. However, existing methods are not designed to infer metastatic seeding patterns. Here we develop a tool, called Treeomics, to reconstruct the phylogeny of metastases and map subclones to their anatomic locations. Treeomics infers comprehensive seeding patterns for pancreatic, ovarian, and prostate cancers. Moreover, Treeomics correctly disambiguates true seeding patterns from sequencing artifacts; 7% of variants were misclassified by conventional statistical methods. These artifacts can skew phylogenies by creating illusory tumour heterogeneity among distinct samples. In silico benchmarking on simulated tumour phylogenies across a wide range of sample purities (15–95%) and sequencing depths (25-800 × ) demonstrates the accuracy of Treeomics compared with existing methods.},
author = {Reiter, Johannes and Makohon Moore, Alvin and Gerold, Jeffrey and Božić, Ivana and Chatterjee, Krishnendu and Iacobuzio Donahue, Christine and Vogelstein, Bert and Nowak, Martin},
issn = {20411723},
journal = {Nature Communications},
publisher = {Nature Publishing Group},
title = {{Reconstructing metastatic seeding patterns of human cancers}},
doi = {10.1038/ncomms14114},
volume = {8},
year = {2017},
}
@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 = {07308566},
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},
}
@inproceedings{950,
abstract = {Two-player games on graphs are widely studied in formal methods as they model the interaction between a system and its environment. The game is played by moving a token throughout a graph to produce an infinite path. There are several common modes to determine how the players move the token through the graph; e.g., in turn-based games the players alternate turns in moving the token. We study the bidding mode of moving the token, which, to the best of our knowledge, has never been studied in infinite-duration games. Both players have separate budgets, which sum up to $1$. In each turn, a bidding takes place. Both players submit bids simultaneously, and a bid is legal if it does not exceed the available budget. The winner of the bidding pays his bid to the other player and moves the token. For reachability objectives, repeated bidding games have been studied and are called Richman games. There, a central question is the existence and computation of threshold budgets; namely, a value t\in [0,1] such that if\PO's budget exceeds $t$, he can win the game, and if\PT's budget exceeds 1-t, he can win the game. We focus on parity games and mean-payoff games. We show the existence of threshold budgets in these games, and reduce the problem of finding them to Richman games. We also determine the strategy-complexity of an optimal strategy. Our most interesting result shows that memoryless strategies suffice for mean-payoff bidding games.
},
author = {Avni, Guy and Henzinger, Thomas A and Chonev, Ventsislav K},
issn = {1868-8969},
location = {Berlin, Germany},
publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik},
title = {{Infinite-duration bidding games}},
doi = {10.4230/LIPIcs.CONCUR.2017.21},
volume = {85},
year = {2017},
}
@inproceedings{949,
abstract = {The notion of treewidth of graphs has been exploited for faster algorithms for several problems arising in verification and program analysis. Moreover, various notions of balanced tree decompositions have been used for improved algorithms supporting dynamic updates and analysis of concurrent programs. In this work, we present a tool for constructing tree-decompositions of CFGs obtained from Java methods, which is implemented as an extension to the widely used Soot framework. The experimental results show that our implementation on real-world Java benchmarks is very efficient. Our tool also provides the first implementation for balancing tree-decompositions. In summary, we present the first tool support for exploiting treewidth in the static analysis problems on Java programs.},
author = {Chatterjee, Krishnendu and Goharshady, Amir and Pavlogiannis, Andreas},
editor = {D'Souza, Deepak},
issn = {03029743},
location = {Pune, India},
pages = {59 -- 66},
publisher = {Springer},
title = {{JTDec: A tool for tree decompositions in soot}},
doi = {10.1007/978-3-319-68167-2_4},
volume = {10482},
year = {2017},
}
@inproceedings{639,
abstract = {We study the problem of developing efficient approaches for proving worst-case bounds of non-deterministic recursive programs. Ranking functions are sound and complete for proving termination and worst-case bounds of non-recursive programs. First, we apply ranking functions to recursion, resulting in measure functions, and show that they provide a sound and complete approach to prove worst-case bounds of non-deterministic recursive programs. Our second contribution is the synthesis of measure functions in non-polynomial forms. We show that non-polynomial measure functions with logarithm and exponentiation can be synthesized through abstraction of logarithmic or exponentiation terms, Farkas’ Lemma, and Handelman’s Theorem using linear programming. While previous methods obtain worst-case polynomial bounds, our approach can synthesize bounds of the form O(n log n) as well as O(nr) where r is not an integer. We present experimental results to demonstrate that our approach can efficiently obtain worst-case bounds of classical recursive algorithms such as Merge-Sort, Closest-Pair, Karatsuba’s algorithm and Strassen’s algorithm.},
author = {Chatterjee, Krishnendu and Fu, Hongfei and Goharshady, Amir},
editor = {Majumdar, Rupak and Kunčak, Viktor},
isbn = {978-331963389-3},
location = {Heidelberg, Germany},
pages = {41 -- 63},
publisher = {Springer},
title = {{Non-polynomial worst case analysis of recursive programs}},
doi = {10.1007/978-3-319-63390-9_3},
volume = {10427},
year = {2017},
}
@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},
}