11,728 research outputs found

    Soft behaviour modelling of user communities

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    A soft modelling approach for describing behaviour in on-line user communities is introduced in this work. Behaviour models of individual users in dynamic virtual environments have been described in the literature in terms of timed transition automata; they have various drawbacks. Soft multi/agent behaviour automata are defined and proposed to describe multiple user behaviours and to recognise larger classes of user group histories, such as group histories which contain unexpected behaviours. The notion of deviation from the user community model allows defining a soft parsing process which assesses and evaluates the dynamic behaviour of a group of users interacting in virtual environments, such as e-learning and e-business platforms. The soft automaton model can describe virtually infinite sequences of actions due to multiple users and subject to temporal constraints. Soft measures assess a form of distance of observed behaviours by evaluating the amount of temporal deviation, additional or omitted actions contained in an observed history as well as actions performed by unexpected users. The proposed model allows the soft recognition of user group histories also when the observed actions only partially meet the given behaviour model constraints. This approach is more realistic for real-time user community support systems, concerning standard boolean model recognition, when more than one user model is potentially available, and the extent of deviation from community behaviour models can be used as a guide to generate the system support by anticipation, projection and other known techniques. Experiments based on logs from an e-learning platform and plan compilation of the soft multi-agent behaviour automaton show the expressiveness of the proposed model

    Towards formal models and languages for verifiable Multi-Robot Systems

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    Incorrect operations of a Multi-Robot System (MRS) may not only lead to unsatisfactory results, but can also cause economic losses and threats to safety. These threats may not always be apparent, since they may arise as unforeseen consequences of the interactions between elements of the system. This call for tools and techniques that can help in providing guarantees about MRSs behaviour. We think that, whenever possible, these guarantees should be backed up by formal proofs to complement traditional approaches based on testing and simulation. We believe that tailored linguistic support to specify MRSs is a major step towards this goal. In particular, reducing the gap between typical features of an MRS and the level of abstraction of the linguistic primitives would simplify both the specification of these systems and the verification of their properties. In this work, we review different agent-oriented languages and their features; we then consider a selection of case studies of interest and implement them useing the surveyed languages. We also evaluate and compare effectiveness of the proposed solution, considering, in particular, easiness of expressing non-trivial behaviour.Comment: Changed formattin

    Engineering Resilient Collective Adaptive Systems by Self-Stabilisation

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    Collective adaptive systems are an emerging class of networked computational systems, particularly suited in application domains such as smart cities, complex sensor networks, and the Internet of Things. These systems tend to feature large scale, heterogeneity of communication model (including opportunistic peer-to-peer wireless interaction), and require inherent self-adaptiveness properties to address unforeseen changes in operating conditions. In this context, it is extremely difficult (if not seemingly intractable) to engineer reusable pieces of distributed behaviour so as to make them provably correct and smoothly composable. Building on the field calculus, a computational model (and associated toolchain) capturing the notion of aggregate network-level computation, we address this problem with an engineering methodology coupling formal theory and computer simulation. On the one hand, functional properties are addressed by identifying the largest-to-date field calculus fragment generating self-stabilising behaviour, guaranteed to eventually attain a correct and stable final state despite any transient perturbation in state or topology, and including highly reusable building blocks for information spreading, aggregation, and time evolution. On the other hand, dynamical properties are addressed by simulation, empirically evaluating the different performances that can be obtained by switching between implementations of building blocks with provably equivalent functional properties. Overall, our methodology sheds light on how to identify core building blocks of collective behaviour, and how to select implementations that improve system performance while leaving overall system function and resiliency properties unchanged.Comment: To appear on ACM Transactions on Modeling and Computer Simulatio

    Enterprise engineering using semantic technologies

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    Modern Enterprises are facing unprecedented challenges in every aspect of their businesses: from marketing research, invention of products, prototyping, production, sales to billing. Innovation is the key to enhancing enterprise performances and knowledge is the main driving force in creating innovation. The identification and effective management of valuable knowledge, however, remains an illusive topic. Knowledge management (KM) techniques, such as enterprise process modelling, have long been recognised for their value and practiced as part of normal business. There are plentiful of KM techniques. However, what is still lacking is a holistic KM approach that enables one to fully connect KM efforts with existing business knowledge and practices already in IT systems, such as organisational memories. To address this problem, we present an integrated three-dimensional KM approach that supports innovative semantics technologies. Its automated formal methods allow us to tap into modern business practices and capitalise on existing knowledge. It closes the knowledge management cycle with user feedback loops. Since we are making use of reliable existing knowledge and methods, new knowledge can be extracted with less effort comparing with another method where new information has to be created from scratch

    Antifragility = Elasticity + Resilience + Machine Learning: Models and Algorithms for Open System Fidelity

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    We introduce a model of the fidelity of open systems - fidelity being interpreted here as the compliance between corresponding figures of interest in two separate but communicating domains. A special case of fidelity is given by real-timeliness and synchrony, in which the figure of interest is the physical and the system's notion of time. Our model covers two orthogonal aspects of fidelity, the first one focusing on a system's steady state and the second one capturing that system's dynamic and behavioural characteristics. We discuss how the two aspects correspond respectively to elasticity and resilience and we highlight each aspect's qualities and limitations. Finally we sketch the elements of a new model coupling both of the first model's aspects and complementing them with machine learning. Finally, a conjecture is put forward that the new model may represent a first step towards compositional criteria for antifragile systems.Comment: Preliminary version submitted to the 1st International Workshop "From Dependable to Resilient, from Resilient to Antifragile Ambients and Systems" (ANTIFRAGILE 2014), https://sites.google.com/site/resilience2antifragile

    Design and Optimisation of the FlyFast Front-end for Attribute-based Coordination

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    Collective Adaptive Systems (CAS) consist of a large number of interacting objects. The design of such systems requires scalable analysis tools and methods, which have necessarily to rely on some form of approximation of the system's actual behaviour. Promising techniques are those based on mean-field approximation. The FlyFast model-checker uses an on-the-fly algorithm for bounded PCTL model-checking of selected individual(s) in the context of very large populations whose global behaviour is approximated using deterministic limit mean-field techniques. Recently, a front-end for FlyFast has been proposed which provides a modelling language, PiFF in the sequel, for the Predicate-based Interaction for FlyFast. In this paper we present details of PiFF design and an approach to state-space reduction based on probabilistic bisimulation for inhomogeneous DTMCs.Comment: In Proceedings QAPL 2017, arXiv:1707.0366

    Social dynamics in conferences: analyses of data from the Live Social Semantics application

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    Popularity and spread of online social networking in recent years has given a great momentum to the study of dynamics and patterns of social interactions. However, these studies have often been confined to the online world, neglecting its interdependencies with the offline world. This is mainly due to the lack of real data that spans across this divide. The Live Social Semantics application is a novel platform that dissolves this divide, by collecting and integrating data about people from (a) their online social networks and tagging activities from popular social networking sites, (b) their publications and co-authorship networks from semantic repositories, and (c) their real-world face-to-face contacts with other attendees collected via a network of wearable active sensors. This paper investigates the data collected by this application during its deployment at three major conferences, where it was used by more than 400 people. Our analyses show the robustness of the patterns of contacts at various conferences, and the influence of various personal properties (e.g. seniority, conference attendance) on social networking patterns
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