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

Ambiguity in high definition: Gaze determines physical interpretation of ambiguous rotation even in the absence of a visual context

YesPhysical interactions between objects, or between an object and the ground, are amongst the most biologically relevant for live beings. Prior knowledge of Newtonian physics may play a role in disambiguating an object’s movement as well as foveation by increasing the spatial resolution of the visual input. Observers were shown a virtual 3D scene, representing an ambiguously rotating ball translating on the ground. The ball was perceived as rotating congruently with friction, but only when gaze was located at the point of contact. Inverting or even removing the visual context had little influence on congruent judgements compared with the effect of gaze. Counterintuitively, gaze at the point of contact determines the solution of perceptual ambiguity, but independently of visual context. We suggest this constitutes a frugal strategy, by which the brain infers dynamics locally when faced with a foveated input that is ambiguous.J.S. was funded by a College of Life Sciences studentship from the University of Leicester

Iterative, Probabilistic Classification Using Uncertain Information

The authors have constructed an iterative, probabilistic reasoning architecture for classification problems. A number of assumptions of conditional independence have been employed in this architecture to derive two iterative updating methods, S and D. A Bayesian network was constructed and the results compared with the iterative methods. Method S and the network are both insensitive to the order of evidence, but do not produce the same results. Further investigation of the nature of these differences is warranted. It is suggested that additional information carried in the network may allow uncertain evidence to be used more effectively than in the iterative methods

A Comparison of Probabilistic Methods for Classification

The authors study a class of problems in which the characteristics of the objects in the frame of discernment U=(u/sub 1/,. . ., u/sub n/) are represented probabilistically. A hypothesis is defined by attributes A/sub 1/,. . .,A/sub s/ which takes values from the sets V/sub 1/,. . .,V/sub s/, respectively. Domain information describing a hypothesis specifies the probability of each attribute A/sub i/ assuming the values from V/sub i/. The domain information concerning attribute A/sub i/ is given by a matrix. The generation of support is driven by the acquisition of evidence concerning attribute values. To compare evidential support generation a simple urn model is constructed to provide the probabilistic domain information. An attribute-value domain is constructed to provide a baseline by which to compare the support generated by an iterative updating architecture, a belief network, and the Dempster-Shafer theory of evidential reasoning

A Probabilistic Iterative Architecture for Classification

A classification architecture that uses probabilistic representation of support and conditionalization and expectation for updating belief is presented. The updating is guided by a utility function that determines the type of information to be acquired. Expected entropy is used as the utility measure. The three major components of a classification system are the representation of the domain information, the evidence, and the support updating paradigm. The representative of domain knowledge and evidence is described. A general overview of the classification architecture is given. The computations and assumptions required in this iterative method are presented. A detailed example illustrating the generation of support based on the acquisition of one item of evidence is given

A Probabilistic Iterative Architecture for Classification

A classification architecture that uses probabilistic representation of support and conditionalization and expectation for updating belief is presented. The updating is guided by a utility function that determines the type of information to be acquired. Expected entropy is used as the utility measure. The three major components of a classification system are the representation of the domain information, the evidence, and the support updating paradigm. The representative of domain knowledge and evidence is described. A general overview of the classification architecture is given. The computations and assumptions required in this iterative method are presented. A detailed example illustrating the generation of support based on the acquisition of one item of evidence is given

A Comparison of Probabilistic Methods for Classification

The authors study a class of problems in which the characteristics of the objects in the frame of discernment U=(u/sub 1/,. . ., u/sub n/) are represented probabilistically. A hypothesis is defined by attributes A/sub 1/,. . .,A/sub s/ which takes values from the sets V/sub 1/,. . .,V/sub s/, respectively. Domain information describing a hypothesis specifies the probability of each attribute A/sub i/ assuming the values from V/sub i/. The domain information concerning attribute A/sub i/ is given by a matrix. The generation of support is driven by the acquisition of evidence concerning attribute values. To compare evidential support generation a simple urn model is constructed to provide the probabilistic domain information. An attribute-value domain is constructed to provide a baseline by which to compare the support generated by an iterative updating architecture, a belief network, and the Dempster-Shafer theory of evidential reasoning