The reactive synthesis problem is to compute a system satisfying a given specification in temporal logic. Bounded synthesis is the approach to bound the maximum size of the system that we accept as a solution to the reactive synthesis problem. As a result, bounded synthesis is decidable whenever the corresponding verification problem is decidable, and can be applied in settings where classic synthesis fails, such as in the synthesis of distributed systems. In this paper, we study the constraint solving problem behind bounded synthesis. We consider different reductions of the bounded synthesis problem of linear-time temporal logic (LTL) to constraint systems given as boolean formulas (SAT), quantified boolean formulas (QBF), and dependency quantified boolean formulas (DQBF). The reductions represent different trade-offs between conciseness and algorithmic efficiency. In the SAT encoding, both inputs and states of the system are represented explicitly; in QBF, inputs are symbolic and states are explicit; in DQBF, both inputs and states are symbolic. We evaluate the encodings systematically using benchmarks from the reactive synthesis competition (SYNTCOMP) and state-of-theart solvers. Our key, and perhaps surprising, empirical finding is that QBF clearly dominates both SAT and DQBF.

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We introduce Parametric Linear Dynamic Logic (PLDL), which extends Linear Dynamic Logic (LDL) by temporal operators equipped with parameters that bound their scope. LDL was proposed as an extension of Linear Temporal Logic (LTL) that is able to express all ω-regular specifications while still maintaining many of LTL's desirable properties like an intuitive syntax and a translation into non-deterministic Büchi automata of exponential size. But LDL lacks capabilities to express timing constraints. By adding parameterized operators to LDL, we obtain a logic that is able to express all ω-regular properties and that subsumes parameterized extensions of LTL like Parametric LTL and PROMPT-LTL.Our main technical contribution is a translation of PLDL formulas into non-deterministic Büchi word automata of exponential size via alternating automata. This yields a PSPACE model checking algorithm and a realizability algorithm with doubly-exponential running time. Furthermore, we give tight upper and lower bounds on optimal parameter values for both problems. These results show that PLDL model checking and realizability are not harder than LTL model checking and realizability.

With ever increasing autonomy of cyber-physical systems, monitoring becomes an integral part for ensuring the safety of the system at runtime. StreamLAB is a monitoring framework with high degree of expressibility and strong correctness guarantees. Specifications are written in RTLola, a stream-based specification language with formal semantics. StreamLAB provides an extensive analysis of the specification, including the computation of memory consumption and run-time guarantees. We demonstrate the applicability of StreamLAB on typical monitoring tasks for cyber-physical systems, such as sensor validation and system health checks.

Aiming at the problem of rapid processing of real-time data in intelligent dispatching, a new method for calculating the information flow of distribution network monitoring is proposed. Access the distribution network monitoring data by publishing subscriptions, combining the topology parallel model of flow calculation, comprehensive use of multi-theme partition message caching technology, to achieve low-latency and high-throughput processing of distribution network monitoring information[1-3]. The method can obtain hundreds of millisecond monitoring data processing delay, and it has practical value in research to improve low-latency distribution network dispatch system.