Building on the work by Fainekos and Pappas and the one by Donzé and Maler, we introduce AvSTL, an extension of metric interval temporal logic by averaged temporal operators. Its expressivity in capturing both space and time robustness helps solving falsification problems (searching for a critical path in hybrid system models); it does so by communicating a designer's intention more faithfully to the stochastic optimization engine employed in a falsification solver. We also introduce a sliding window-like algorithm that keeps the cost of computing truth/robustness values tractable.[0,10] (v ≥ 80) ("the velocity reaches 80 km/h within 10 sec.") are 20 and 0, for the green and red signals on the right. Therefore space robustness is a "vertical margin" between a signal and a specification. An efficient algorithm is proposed in [11] for computing this notion of robustness.The notion of robustness is extended in [12] to take time robustness also into account. Consider the same specification [0,10] (v ≥ 80) against the green and red signals on the right. The green one is more robust since it reaches 80 km/h much earlier than the deadline (10 sec.), while the red one barely makes the deadline.The current work continues this line of work, with the slogan that expressivity of temporal logic should help falsification. With more expressivity, a designer's concerns

With the rapid development of software and distributed computing, Cyber-Physical Systems (CPS) are widely adopted in many application areas, e.g., smart grid, autonomous automobile. It is difficult to detect defects in CPS models due to the complexities involved in the software and physical systems. To find defects in CPS models efficiently, robustness guided falsification of CPS is introduced. Existing methods use several optimization techniques to generate counterexamples, which falsify the given properties of a CPS. However those methods may require a large number of simulation runs to find the counterexample and is far from practical. In this work, we explore state-of-the-art Deep Reinforcement Learning (DRL) techniques to reduce the number of simulation runs required to find such counterexamples. We report our method and the preliminary evaluation results.

Abstract. The timed pattern matching problem is formulated by Ulus et al. and has been actively studied since, with its evident application in monitoring realtime systems. The problem takes as input a timed word/signal and a timed pattern (specified either by a timed regular expression or by a timed automaton); and it returns the set of those intervals for which the given timed word, when restricted to the interval, matches the given pattern. We contribute a Boyer-Moore type optimization in timed pattern matching, relying on the classic Boyer-Moore string matching algorithm and its extension to (untimed) pattern matching by Watson and Watson. We assess its effect through experiments; for some problem instances our Boyer-Moore type optimization achieves speed-up by two times, indicating its potential in real-world monitoring tasks where data sets tend to be massive.

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