Learning Moore Machines from Input-Output Traces

The problem of learning automata from example traces (but no equivalence or membership queries) is fundamental in automata learning theory and practice. In this paper we study this problem for finite state machines with inputs and outputs, and in particular for Moore machines. We develop three algorithms for solving this problem: (1) the PTAP algorithm, which transforms a set of input-output traces into an incomplete Moore machine and then completes the machine with self-loops; (2) the PRPNI algorithm, which uses the well-known RPNI algorithm for automata learning to learn a product of automata encoding a Moore machine; and (3) the MooreMI algorithm, which directly learns a Moore machine using PTAP extended with state merging. We prove that MooreMI has the fundamental identification in the limit property. We also compare the algorithms experimentally in terms of the size of the learned machine and several notions of accuracy, introduced in this paper. Finally, we compare with OSTIA, an algorithm that learns a more general class of transducers, and find that OSTIA generally does not learn a Moore machine, even when fed with a characteristic sample

Learning and testing the bounded retransmission protocol

Abstract Using a well-known industrial case study from the verification literature, the bounded retransmission protocol, we show how active learning can be used to establish the correctness of protocol implementation I relative to a given reference implementation R. Using active learning, we learn a model M R of reference implementation R, which serves as input for a model based testing tool that checks conformance of implementation I to M R . In addition, we also explore an alternative approach in which we learn a model M I of implementation I, which is compared to model M R using an equivalence checker. Our work uses a unique combination of software tools for model construction (Uppaal), active learning (LearnLib, Tomte), model-based testing (JTorX, TorXakis) and verification (CADP, MRMC). We show how these tools can be used for learning these models, analyzing the obtained results, and improving the learning performance

Predictors of and reasons for pacifier use in first-time mothers: an observational study

Background: The use of pacifiers is commonplace in Australia and has been shown to be negatively associated with breastfeeding duration. In order to influence behaviour related to the use of pacifiers it is important to understand the reasons for their use. The primary aim of this observational study was to investigate who (if anyone) advises first-time mothers to give a pacifier and the reasons for which they first give (or try to give) a pacifier to their infant. Additionally, this study investigated the predictors of pacifier use and the relationship between pacifier use and breastfeeding duration. Methods: In total, 670 Australian first-time mothers recruited as part of the NOURISH trial completed a questionnaire regarding infant feeding and pacifier use. Results: Pacifiers were introduced by 79% of mothers, of whom 28.7% were advised to use a pacifier by their mother/mother-in-law with a further 22.7% being advised by a midwife. The majority of mothers used a pacifier in order to soothe their infant (78.3%), to help put them to sleep (57.4%) and to keep them comforted and quiet (40.4%). Pacifiers given to infants before four weeks (adjHR 3.67; 95%CI 2.14–6.28) and used most days (adjHR 3.28; 95%CI 1.92–5.61) were significantly associated with shorter duration of breastfeeding. Conclusions: This study identifies an opportunity for educating new mothers and their support network, particularly their infant’s grandmothers, with regards to potential risks associated with the early and frequent use of a pacifier, and alternative methods for soothing their infant, in order to reduce the use of pacifiers and their potentially negative effect on breastfeeding duratio

Understanding emotionally relevant situations in primary care dental practice: 1. Clinical situations and emotional responses

Background and aims. The stressful nature of dental practice is well established. Much less information is available on the coping strategies used by dentists and the emotions which underlie the stressful experience. Previous research has been almost exclusively questionnaire-based, limiting the range of emotions explored. This study used qualitative methods to explore the full extent of emotions and coping strategies associated with stressful events in primary dental practice. Method. Semi-structured interviews were conducted with 20 dentists in Lincoln and the surrounding area. Verbatim transcriptions were analysed using thematic analysis. Results. Participants reported a wide variety of stressful situations, consistent with the existing literature, which were associated with a diverse range of negative emotional responses including anxiety, anger and sadness. Dentists tended to have more difficulty identifying positive events and emotions. The designation of a situation as stressful or otherwise was dependent on the dentist's personal interpretation of the event. Data relating to the effects of stressors and the coping strategies used by dentists will be presented in subsequent papers. Conclusion. The situations which dentists find difficult are accompanied by a diverse set of emotions, rather than omnipresent 'stress.' This has implications for stress management programmes for those in dental practic