Specification sketching for Linear Temporal Logic

Virtually all verification and synthesis techniques assume that the formal specifications are readily available, functionally correct, and fully match the engineer's understanding of the given system. However, this assumption is often unrealistic in practice: formalizing system requirements is notoriously difficult, error-prone, and requires substantial training. To alleviate this severe hurdle, we propose a fundamentally novel approach to writing formal specifications, named specification sketching for Linear Temporal Logic (LTL). The key idea is that an engineer can provide a partial LTL formula, called an LTL sketch, where parts that are hard to formalize can be left out. Given a set of examples describing system behaviors that the specification should or should not allow, the task of a so-called sketching algorithm is then to complete a given sketch such that the resulting LTL formula is consistent with the examples. We show that deciding whether a sketch can be completed falls into the complexity class NP and present two SAT-based sketching algorithms. We also demonstrate that sketching is a practical approach to writing formal specifications using a prototype implementation

Scalable Anytime Algorithms for Learning Fragments

International audienceAbstract Linear temporal logic (LTL) is a specification language for finite sequences (called traces) widely used in program verification, motion planning in robotics, process mining, and many other areas. We consider the problem of learning formulas in fragments of LTL without the $$\mathbf {U}$$ U -operator for classifying traces; despite a growing interest of the research community, existing solutions suffer from two limitations: they do not scale beyond small formulas, and they may exhaust computational resources without returning any result. We introduce a new algorithm addressing both issues: our algorithm is able to construct formulas an order of magnitude larger than previous methods, and it is anytime, meaning that it in most cases successfully outputs a formula, albeit possibly not of minimal size. We evaluate the performances of our algorithm using an open source implementation against publicly available benchmarks

Learning Interpretable Temporal Properties from Positive Examples Only

We consider the problem of explaining the temporal behavior of black-boxsystems using human-interpretable models. To this end, based on recent researchtrends, we rely on the fundamental yet interpretable models of deterministicfinite automata (DFAs) and linear temporal logic (LTL) formulas. In contrast tomost existing works for learning DFAs and LTL formulas, we rely on onlypositive examples. Our motivation is that negative examples are generallydifficult to observe, in particular, from black-box systems. To learnmeaningful models from positive examples only, we design algorithms that relyon conciseness and language minimality of models as regularizers. To this end,our algorithms adopt two approaches: a symbolic and a counterexample-guidedone. While the symbolic approach exploits an efficient encoding of languageminimality as a constraint satisfaction problem, the counterexample-guided onerelies on generating suitable negative examples to prune the search. Both theapproaches provide us with effective algorithms with theoretical guarantees onthe learned models. To assess the effectiveness of our algorithms, we evaluateall of them on synthetic data.<br

Development of corrosion of steel bars embedded in mortar made with slag from secondary metallurgy

The aim of this work is to study the evolution of the corrosion rate of reinforcements embedded in mortar specimens that have been partly or fully replaced by the sand ladle furnace white slag. Prisms are manufactured mortar 6cm x 8cm x 2cm in which are embedded reinforcing steel bars of 6mm diameter B500SD. At the time of mixing were added varying amounts of chloride ion content by weight of cement (0%, 0.4%, 0.8%, 1.2%, 2%). The specimens were made totally or partially replacing the white slag, getting four different mixes depending on the degree of substitution. After curing the specimens for 28 days in moist chambers proceeded to dry up naturally. Here are gradually dampened by its conservation in a moist chamber, periodically measuring the corrosion rate of the bars using the technique of polarization curve. The results, in terms of corrosion current and corrosion potential, were compared with those obtained on standard samples, without replacement by slag aggregate. The analysis of results allows us to know, depending on the type of mortar used, the chloride threshold with the depassivation produced steel and the corrosion rates achieved in steels in the active state in terms of mortar moisture, obtained from qualitatively using gravimetric techniques. The results achieved to date support the conclusion that no significant differences in the behavior against corrosion induced by chloride ions, between the steel bars embedded in standard samples and the steel bars embedded in samples including with aggregates from slag. Both the chloride threshold resulting in the depassivation steel as the corrosion rate reached through the bars in an active state are very similar in both types of mortars when they have the same moisture content

Parameterized Synthesis with Safety Properties

Parameterized synthesis offers a solution to the problem of constructing correct and verified controllers for parameterized systems. Such systems occur naturally in practice (e.g., in the form of distributed protocols where the amount of processes is often unknown at design time and the protocol must work regardless of the number of processes). In this paper, we present a novel learning based approach to the synthesis of reactive controllers for parameterized systems from safety specifications. We use the framework of regular model checking to model the synthesis problem as an infinite-duration two-player game and show how one can utilize Angluin's well-known L* algorithm to learn correct-by-design controllers. This approach results in a synthesis procedure that is conceptually simpler than existing synthesis methods with a completeness guarantee, whenever a winning strategy can be expressed by a regular set. We have implemented our algorithm in a tool called L*-PSynth and have demonstrated its performance on a range of benchmarks, including robotic motion planning and distributed protocols. Despite the simplicity of L*-PSynth it competes well against (and in many cases even outperforms) the state-of-the-art tools for synthesizing parameterized systems.Comment: 18 page

A Change in the Dark Room: The Effects of Human Factors and Cognitive Loading Issues for NextGen TRACON Air Traffic Controllers

By 2020 all aircraft in United States airspace must use ADS-B (Automatic Dependent Surveillance-Broadcast) Out. This is a key component of the Next Generation (NextGen) Air Transportation System, which marks the first time all aircraft will be tracked continuously using satellites instead of ground-based radar. Standard Terminal Automation Replacement System (STARS) in the Terminal Radar Approach Control (TRACON) is a primary NextGen upgrade where digitized automation/information surrounds STARS controllers while controlling aircraft. Applying the SHELL model, the authors analyze human factors changes affecting TRACON controllers from pre-STARS technology through NextGen technologies on performance. Results of an informal survey of STARS controllers assessed cognitive processing issues and indicates the greatest concern is with movements to view other displays and added time to re-engage STARS

Object Detection Through Exploration With A Foveated Visual Field

We present a foveated object detector (FOD) as a biologically-inspired alternative to the sliding window (SW) approach which is the dominant method of search in computer vision object detection. Similar to the human visual system, the FOD has higher resolution at the fovea and lower resolution at the visual periphery. Consequently, more computational resources are allocated at the fovea and relatively fewer at the periphery. The FOD processes the entire scene, uses retino-specific object detection classifiers to guide eye movements, aligns its fovea with regions of interest in the input image and integrates observations across multiple fixations. Our approach combines modern object detectors from computer vision with a recent model of peripheral pooling regions found at the V1 layer of the human visual system. We assessed various eye movement strategies on the PASCAL VOC 2007 dataset and show that the FOD performs on par with the SW detector while bringing significant computational cost savings.Comment: An extended version of this manuscript was published in PLOS Computational Biology (October 2017) at https://doi.org/10.1371/journal.pcbi.100574

Striatal Volume Predicts Level of Video Game Skill Acquisition

Video game skills transfer to other tasks, but individual differences in performance and in learning and transfer rates make it difficult to identify the source of transfer benefits. We asked whether variability in initial acquisition and of improvement in performance on a demanding video game, the Space Fortress game, could be predicted by variations in the pretraining volume of either of 2 key brain regions implicated in learning and memory: the striatum, implicated in procedural learning and cognitive flexibility, and the hippocampus, implicated in declarative memory. We found that hippocampal volumes did not predict learning improvement but that striatal volumes did. Moreover, for the striatum, the volumes of the dorsal striatum predicted improvement in performance but the volumes of the ventral striatum did not. Both ventral and dorsal striatal volumes predicted early acquisition rates. Furthermore, this early-stage correlation between striatal volumes and learning held regardless of the cognitive flexibility demands of the game versions, whereas the predictive power of the dorsal striatal volumes held selectively for performance improvements in a game version emphasizing cognitive flexibility. These findings suggest a neuroanatomical basis for the superiority of training strategies that promote cognitive flexibility and transfer to untrained tasks.United States. Office of Naval Research (grant number N00014-07-1-0903