Talking quiescence: a rigorous theory that supports parallel composition, action hiding and determinisation

The notion of quiescence - the absence of outputs - is vital in both behavioural modelling and testing theory. Although the need for quiescence was already recognised in the 90s, it has only been treated as a second-class citizen thus far. This paper moves quiescence into the foreground and introduces the notion of quiescent transition systems (QTSs): an extension of regular input-output transition systems (IOTSs) in which quiescence is represented explicitly, via quiescent transitions. Four carefully crafted rules on the use of quiescent transitions ensure that our QTSs naturally capture quiescent behaviour. We present the building blocks for a comprehensive theory on QTSs supporting parallel composition, action hiding and determinisation. In particular, we prove that these operations preserve all the aforementioned rules. Additionally, we provide a way to transform existing IOTSs into QTSs, allowing even IOTSs as input that already contain some quiescent transitions. As an important application, we show how our QTS framework simplifies the fundamental model-based testing theory formalised around ioco.Comment: In Proceedings MBT 2012, arXiv:1202.582

4-1 旧世界ザル類のY染色体進化に関る分子マーカー作製と比較マッピング(X.共同利用研究 2.研究成果)

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)2895212-22

Diagnosing differences between business process models

This paper presents a technique to diagnose differences between business process models in the EPC notation. The diagnosis returns the exact position of a difference in the business process models and diagnoses the type of a difference, using a typology of differences developed in previous work. This in contrast to existing techniques for detecting process differences (by showing non-equivalence), which return simple true/false statements, or statements in terms of a formal semantics. Neither type of statement is helpful to a business analyst not versed in formal semantics. A case study illustrates the usefulness of the technique. It also shows that, although the technique has exponential complexity, it can be used in practice, because of repeated scoping of the models. The technique can be used, for example, to resolve differences between operational process in a merger between organizations

Software model synthesis using satisfiability solvers

Contains fulltext : 103766.pdf (preprint version ) (Open Access

1,135 Genomes Reveal the Global Pattern of Polymorphism in Arabidopsis thaliana

Arabidopsis thaliana serves as a model organism for the study of fundamental physiological, cellular, and molecular processes. It has also greatly advanced our understanding of intraspecific genome variation. We present a detailed map of variation in 1,135 high-quality re-sequenced natural inbred lines representing the native Eurasian and North African range and recently colonized North America. We identify relict populations that continue to inhabit ancestral habitats, primarily in the Iberian Peninsula. They have mixed with a lineage that has spread to northern latitudes from an unknown glacial refugium and is now found in a much broader spectrum of habitats. Insights into the history of the species and the fine-scale distribution of genetic diversity provide the basis for full exploitation of A. thaliana natural variation through integration of genomes and epigenomes with molecular and non-molecular phenotypes.Peer Reviewe

Improving active mealy machine learning for protocol conformance testing

Contains fulltext : 122418.pdf (preprint version ) (Open Access

An Exploratory Robot Controller which Adapts to Unknown Environments and Damaged Sensors

In this paper we describe an adaptive mobile robot control system that enables a multi-sensor robot to learn reactive behaviours by interacting with the environment. The controller is particularly suitable for exploratory robots due to its ability to adapt to unknown environments and recover from partial sensor damage. Learning is based on the robot learning a map between sensors and trajectory velocities so at any instant the robot becomes capable of realising how fast it should move along its predefined trajectories. Behaviours are performed by selecting trajectories based on their velocity and closeness to a preset behaviour criteria. Unlike reinforcement learning, the map can be obtained relatively quickly by extracting knowledge directly form the environment via the sensors thereby avoiding the credit assignment problem. We demonstrate the effectiveness of this approach to robot learning by using a Yamabico mobile robot to firstly acquire goal seeking behaviour and then recover from damage inflicted on its sensors. 1