TY - CONF AB - We designed and implemented a new programming language called Hierarchical Timing Language (HTL) for hard realtime systems. Critical timing constraints are specified within the language,and ensured by the compiler. Programs in HTL are extensible in two dimensions without changing their timing behavior: new program modules can be added, and individual program tasks can be refined. The mechanism supporting time invariance under parallel composition is that different program modules communicate at specified instances of time. Time invariance under refinement is achieved by conservative scheduling of the top level. HTL is a coordination language, in that individual tasks can be implemented in "foreign" languages. As a case study, we present a distributed HTL implementation of an automotive steer-by-wire controller. AU - Ghosal, Arkadeb AU - Thomas Henzinger AU - Iercan, Daniel AU - Kirsch, Christoph M AU - Sangiovanni-Vincentelli, Alberto ID - 4526 TI - A hierarchical coordination language for interacting real-time tasks ER - TY - CONF AB - Computational modeling of biological systems is becoming increasingly common as scientists attempt to understand biological phenomena in their full complexity. Here we distinguish between two types of biological models mathematical and computational - according to their different representations of biological phenomena and their diverse potential. We call the approach of constructing computational models of biological systems executable biology, as it focuses on the design of executable computer algorithms that mimic biological phenomena. We give an overview of the main modeling efforts in this direction, and discuss some of the new challenges that executable biology poses for computer science and biology. We argue that for executable biology to reach its full potential as a mainstream biological technique, formal and algorithmic approaches must be integrated into biological research, driving biology towards a more precise engineering discipline. AU - Fisher, Jasmin AU - Thomas Henzinger ID - 4528 TI - Executable biology ER - TY - CONF AB - Games on graphs with ω-regular objectives provide a model for the control and synthesis of reactive systems. Every ω-regular objective can be decomposed into a safety part and a liveness part. The liveness part ensures that something good happens “eventually.” Two main strengths of the classical, infinite-limit formulation of liveness are robustness (independence from the granularity of transitions) and simplicity (abstraction of complicated time bounds). However, the classical liveness formulation suffers from the drawback that the time until something good happens may be unbounded. A stronger formulation of liveness, so-called finitary liveness, overcomes this drawback, while still retaining robustness and simplicity. Finitary liveness requires that there exists an unknown, fixed bound b such that something good happens within b transitions. While for one-shot liveness (reachability) objectives, classical and finitary liveness coincide, for repeated liveness (Büchi) objectives, the finitary formulation is strictly stronger. In this work we study games with finitary parity and Streett (fairness) objectives. We prove the determinacy of these games, present algorithms for solving these games, and characterize the memory requirements of winning strategies. Our algorithms can be used, for example, for synthesizing controllers that do not let the response time of a system increase without bound. AU - Krishnendu Chatterjee AU - Thomas Henzinger ID - 4539 TI - Finitary winning in omega-regular games VL - 3920 ER - TY - CONF AB - A stochastic graph game is played by two players on a game graph with probabilistic transitions. We consider stochastic graph games with ω-regular winning conditions specified as parity objectives. These games lie in NP ∩ coNP. We present a strategy improvement algorithm for stochastic parity games; this is the first non-brute-force algorithm for solving these games. From the strategy improvement algorithm we obtain a randomized subexponential-time algorithm to solve such games. AU - Krishnendu Chatterjee AU - Thomas Henzinger ID - 4538 TI - Strategy improvement and randomized subexponential algorithms for stochastic parity games VL - 3884 ER - TY - CONF AB - We consider Markov decision processes (MDPs) with multiple discounted reward objectives. Such MDPs occur in design problems where one wishes to simultaneously optimize several criteria, for example, latency and power. The possible trade-offs between the different objectives are characterized by the Pareto curve. We show that every Pareto-optimal point can be achieved by a memoryless strategy; however, unlike in the single-objective case, the memoryless strategy may require randomization. Moreover, we show that the Pareto curve can be approximated in polynomial time in the size of the MDP. Additionally, we study the problem if a given value vector is realizable by any strategy, and show that it can be decided in polynomial time; but the question whether it is realizable by a deterministic memoryless strategy is NP-complete. These results provide efficient algorithms for design exploration in MDP models with multiple objectives. This research was supported in part by the AFOSR MURI grant F49620-00-1-0327, and the NSF grants CCR-0225610, CCR-0234690, and CCR-0427202. AU - Krishnendu Chatterjee AU - Majumdar, Ritankar S AU - Thomas Henzinger ID - 4551 TI - Markov decision processes with multiple objectives VL - 3884 ER -