TY - CONF AB - We solve the open problems of translating, when possible, all common classes of nondeterministic word automata to deterministic and nondeterministic co-Büchi word automata. The handled classes include Büchi, parity, Rabin, Streett and Muller automata. The translations follow a unified approach and are all asymptotically tight. The problem of translating Büchi automata to equivalent co-Büchi automata was solved in [2], leaving open the problems of translating automata with richer acceptance conditions. For these classes, one cannot easily extend or use the construction in [2]. In particular, going via an intermediate Büchi automaton is not optimal and might involve a blow-up exponentially higher than the known lower bound. Other known translations are also not optimal and involve a doubly exponential blow-up. We describe direct, simple, and asymptotically tight constructions, involving a 2Θ(n) blow-up. The constructions are variants of the subset construction, and allow for symbolic implementations. Beyond the theoretical importance of the results, the new constructions have various applications, among which is an improved algorithm for translating, when possible, LTL formulas to deterministic Büchi word automata. AU - Boker, Udi AU - Kupferman, Orna ED - Hofmann, Martin ID - 3327 TI - Co-Büching them all VL - 6604 ER - TY - CONF AB - Playing table tennis is a difficult task for robots, especially due to their limitations of acceleration. A key bottleneck is the amount of time needed to reach the desired hitting position and velocity of the racket for returning the incoming ball. Here, it often does not suffice to simply extrapolate the ball's trajectory after the opponent returns it but more information is needed. Humans are able to predict the ball's trajectory based on the opponent's moves and, thus, have a considerable advantage. Hence, we propose to incorporate an anticipation system into robot table tennis players, which enables the robot to react earlier while the opponent is performing the striking movement. Based on visual observation of the opponent's racket movement, the robot can predict the aim of the opponent and adjust its movement generation accordingly. The policies for deciding how and when to react are obtained by reinforcement learning. We conduct experiments with an existing robot player to show that the learned reaction policy can significantly improve the performance of the overall system. AU - Wang, Zhikun AU - Lampert, Christoph AU - Mülling, Katharina AU - Schölkopf, Bernhard AU - Peters, Jan ID - 3337 TI - Learning anticipation policies for robot table tennis ER - TY - GEN AB - Turn-based stochastic games and its important subclass Markov decision processes (MDPs) provide models for systems with both probabilistic and nondeterministic behaviors. We consider turn-based stochastic games with two classical quantitative objectives: discounted-sum and long-run average objectives. The game models and the quantitative objectives are widely used in probabilistic verification, planning, optimal inventory control, network protocol and performance analysis. Games and MDPs that model realistic systems often have very large state spaces, and probabilistic abstraction techniques are necessary to handle the state-space explosion. The commonly used full-abstraction techniques do not yield space-savings for systems that have many states with similar value, but does not necessarily have similar transition structure. A semi-abstraction technique, namely Magnifying-lens abstractions (MLA), that clusters states based on value only, disregarding differences in their transition relation was proposed for qualitative objectives (reachability and safety objectives). In this paper we extend the MLA technique to solve stochastic games with discounted-sum and long-run average objectives. We present the MLA technique based abstraction-refinement algorithm for stochastic games and MDPs with discounted-sum objectives. For long-run average objectives, our solution works for all MDPs and a sub-class of stochastic games where every state has the same value. AU - Chatterjee, Krishnendu AU - De Alfaro, Luca AU - Pritam, Roy ID - 3339 T2 - arXiv TI - Magnifying lens abstraction for stochastic games with discounted and long-run average objectives ER - TY - CONF AB - We consider Markov decision processes (MDPs) with ω-regular specifications given as parity objectives. We consider the problem of computing the set of almost-sure winning states from where the objective can be ensured with probability 1. The algorithms for the computation of the almost-sure winning set for parity objectives iteratively use the solutions for the almost-sure winning set for Büchi objectives (a special case of parity objectives). Our contributions are as follows: First, we present the first subquadratic symbolic algorithm to compute the almost-sure winning set for MDPs with Büchi objectives; our algorithm takes O(nm) symbolic steps as compared to the previous known algorithm that takes O(n 2) symbolic steps, where n is the number of states and m is the number of edges of the MDP. In practice MDPs often have constant out-degree, and then our symbolic algorithm takes O(nn) symbolic steps, as compared to the previous known O(n 2) symbolic steps algorithm. Second, we present a new algorithm, namely win-lose algorithm, with the following two properties: (a) the algorithm iteratively computes subsets of the almost-sure winning set and its complement, as compared to all previous algorithms that discover the almost-sure winning set upon termination; and (b) requires O(nK) symbolic steps, where K is the maximal number of edges of strongly connected components (scc’s) of the MDP. The win-lose algorithm requires symbolic computation of scc’s. Third, we improve the algorithm for symbolic scc computation; the previous known algorithm takes linear symbolic steps, and our new algorithm improves the constants associated with the linear number of steps. In the worst case the previous known algorithm takes 5·n symbolic steps, whereas our new algorithm takes 4 ·n symbolic steps. AU - Chatterjee, Krishnendu AU - Henzinger, Monika H AU - Joglekar, Manas AU - Nisarg, Shah ED - Gopalakrishnan, Ganesh ED - Qadeer, Shaz ID - 3342 TI - Symbolic algorithms for qualitative analysis of Markov decision processes with Büchi objectives VL - 6806 ER - TY - CONF AB - The class of omega-regular languages provides a robust specification language in verification. Every omega-regular condition can be decomposed into a safety part and a liveness part. The liveness part ensures that something good happens "eventually". Finitary liveness was proposed by Alur and Henzinger as a stronger formulation of liveness. It requires that there exists an unknown, fixed bound b such that something good happens within b transitions. In this work we consider automata with finitary acceptance conditions defined by finitary Buchi, parity and Streett languages. We study languages expressible by such automata: we give their topological complexity and present a regular-expression characterization. We compare the expressive power of finitary automata and give optimal algorithms for classical decisions questions. We show that the finitary languages are Sigma 2-complete; we present a complete picture of the expressive power of various classes of automata with finitary and infinitary acceptance conditions; we show that the languages defined by finitary parity automata exactly characterize the star-free fragment of omega B-regular languages; and we show that emptiness is NLOGSPACE-complete and universality as well as language inclusion are PSPACE-complete for finitary parity and Streett automata. AU - Chatterjee, Krishnendu AU - Fijalkow, Nathanaël ID - 3347 TI - Finitary languages VL - 6638 ER -