An Analysis Pathway for the Quantitative Evaluation of Public Transport Systems

We consider the problem of evaluating quantitative service-level agreements in public services such as transportation systems. We describe the integration of quantitative analysis tools for data fitting, model generation, simulation, and statistical model-checking, creating an analysis pathway leading from system measurement data to verification results. We apply our pathway to the problem of determining whether public bus systems are delivering an appropriate quality of service as required by regulators. We exercise the pathway on service data obtained from Lothian Buses about the arrival and departure times of their buses on key bus routes through the city of Edinburgh. Although we include only that example in the present paper, our methods are sufficiently general to apply to other transport systems and other cities

Statistical Model Checking for Product Lines

International audienceWe report on the suitability of statistical model checking forthe analysis of quantitative properties of product line models by an extendedtreatment of earlier work by the authors. The type of analysis thatcan be performed includes the likelihood of specific product behaviour,the expected average cost of products (in terms of the attributes of theproducts’ features) and the probability of features to be (un)installed atruntime. The product lines must be modelled in QFLan, which extendsthe probabilistic feature-oriented language PFLan with novel quantitativeconstraints among features and on behaviour and with advancedfeature installation options. QFLan is a rich process-algebraic specifi-cation language whose operational behaviour interacts with a store ofconstraints, neatly separating product configuration from product behaviour.The resulting probabilistic configurations and probabilistic behaviourconverge in a discrete-time Markov chain semantics, enablingthe analysis of quantitative properties. Technically, a Maude implementationof QFLan, integrated with Microsoft’s SMT constraint solver Z3,is combined with the distributed statistical model checker MultiVeStA,developed by one of the authors. We illustrate the feasibility of our frameworkby applying it to a case study of a product line of bikes

Optimization of global production scheduling with deep reinforcement learning

Industrie 4.0 introduces decentralized, self-organizing and self-learning systems for production control. At the same time, new machine learning algorithms are getting increasingly powerful and solve real world problems. We apply Google DeepMind's Deep Q Network (DQN) agent algorithm for Reinforcement Learning (RL) to production scheduling to achieve the Industrie 4.0 vision for production control. In an RL environment cooperative DQN agents, which utilize deep neural networks, are trained with user-defined objectives to optimize scheduling. We validate our system with a small factory simulation, which is modeling an abstracted frontend-of-line semiconductor production facility

Song learning in domesticated canaries in a restricted acoustic environment

Many songbirds learn their songs early in life from a song model. In the absence of such a model, they develop an improvised song that often lacks the species-typical song structure. Open-ended learners, such as the domesticated canary, are able to modify their songs in adulthood, although the mechanisms that guide and time the song-learning process are still not fully understood. In a previous study, we showed that male domesticated canaries lacking an adult song model in their first year substantially change their song repertoire and composition when exposed to normally reared conspecifics in their second year. Here, we investigate song development in descendants of canaries that were raised and kept as a peer group without a song model. Such males represent tutors with abnormal song characteristics. Interestingly, the F1 generation developed quite normal song structure, and when brought into an environment with normally raised canaries in their second year, they did not modify their songs substantially. These results suggest that contact with an adult song model early in life is crucial for song crystallization, but also that song development is at least partly guided by innate rules. They also question the existing classification of canaries as open-ended learners