We introduce Stacked Thompson Bandits (STB) for efficiently generating plans that are likely to satisfy a given bounded temporal logic requirement. STB uses a simulation for evaluation of plans, and takes a Bayesian approach to using the resulting information to guide its search. In particular, we show that stacking multiarmed bandits and using Thompson sampling to guide the action selection process for each bandit enables STB to generate plans that satisfy requirements with a high probability while only searching a fraction of the search space.
Our work on STB is strongly influenced by existing open loop planners. In particular, Cross Entropy Open Loop Planning is an approach for planning in large-scale continuous MDPs @cite_1 . It is however not applicable to discrete domains as STB. Recently, cross entropy planning has been used for searching sequences that satisfy a given temporal logic formula @cite_9 in a continuous motion planning setting.
@cite_9 @cite_1
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Randomized testing is a popular approach for checking properties of large embedded system designs. It is well known that a uniform random choice of test inputs is often sub-optimal. Ideally, the choice of inputs has to be guided by choosing the right input distributions in order to expose corner-case violations. However, this is also known to be a hard problem, in practice. In this paper, we present an application of the cross-entropy method for adaptively choosing input distributions for falsifying temporal logic properties of hybrid systems. We present various choices for representing input distribution families for the cross-entropy method, ranging from a complete partitioning of the input space into cells to a factored distribution of the input using graphical models. Finally, we experimentally compare the falsification approach using the cross-entropy method to other stochastic and heuristic optimization techniques implemented inside the tool S-Taliro over a set of benchmark systems. The performance of the cross entropy method is quite promising. We find that sampling inputs using the cross-entropy method guided by trace robustness can discover violations faster, and more consistently than the other competing methods considered. We introduce Stacked Thompson Bandits (STB) for efficiently generating plans that are likely to satisfy a given bounded temporal logic requirement. STB uses a simulation for evaluation of plans, and takes a Bayesian approach to using the resulting information to guide its search. In particular, we show that stacking multiarmed bandits and using Thompson sampling to guide the action selection process for each bandit enables STB to generate plans that satisfy requirements with a high probability while only searching a fraction of the search space. Monitoring transient behaviors of real-time systems plays an important role in model-based systems design. Signal Temporal Logic (STL) emerges as a convenient and powerful formalism for continuous and hybrid systems. This paper presents an efficient algorithm for computing the robustness degree in which a piecewise-continuous signal satisfies or violates an STL formula. The algorithm, by leveraging state-of-the-art streaming algorithms from Signal Processing, is linear in the size of the signal and its implementation in the Breach tool is shown to outperform alternative implementations. The next-generation mobile network anticipates integrated heterogeneous fronthaul and backhaul technologies referred to as a unified crosshaul architecture. The crosshaul enables a flexible and cost-efficient infrastructure for handling mobile data tsunami from dense Internet of things (IoT). However, stabilization, energy efficiency, and latency have not been jointly considered in the optimization of crosshaul performance. To overcome these issues, we propose an orchestration scheme referred to as the stabilized green crosshaul orchestration (SGCO). SGCO utilizes a Lyapunov-theory-based drift-plus-penalty policy to determine the optimal amount of offloaded data that should be processed either at the eastbound or westbound computing platforms to minimize energy consumption. To achieve system stability, the cache buffer is considered as the main constraint in developing the optimization process. Moreover, the amount of offloaded data transmitted via crosshaul links is selected by adopting the binary min-knapsack problem. Accordingly, a lightweight heuristic algorithm is proposed. As the cache buffer is stabilized and the computations are controlled, the SGCO ensures adjustable computing latency threshold for various IoT services. The performance analysis shows that the proposed SGCO scheme exposes effective energy consumption compared to other existing schemes while maintaining system stability considering latency.