54 research outputs found

    Artificial Collective Intelligence Engineering: a Survey of Concepts and Perspectives

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    Collectiveness is an important property of many systems--both natural and artificial. By exploiting a large number of individuals, it is often possible to produce effects that go far beyond the capabilities of the smartest individuals, or even to produce intelligent collective behaviour out of not-so-intelligent individuals. Indeed, collective intelligence, namely the capability of a group to act collectively in a seemingly intelligent way, is increasingly often a design goal of engineered computational systems--motivated by recent techno-scientific trends like the Internet of Things, swarm robotics, and crowd computing, just to name a few. For several years, the collective intelligence observed in natural and artificial systems has served as a source of inspiration for engineering ideas, models, and mechanisms. Today, artificial and computational collective intelligence are recognised research topics, spanning various techniques, kinds of target systems, and application domains. However, there is still a lot of fragmentation in the research panorama of the topic within computer science, and the verticality of most communities and contributions makes it difficult to extract the core underlying ideas and frames of reference. The challenge is to identify, place in a common structure, and ultimately connect the different areas and methods addressing intelligent collectives. To address this gap, this paper considers a set of broad scoping questions providing a map of collective intelligence research, mostly by the point of view of computer scientists and engineers. Accordingly, it covers preliminary notions, fundamental concepts, and the main research perspectives, identifying opportunities and challenges for researchers on artificial and computational collective intelligence engineering.Comment: This is the author's final version of the article, accepted for publication in the Artificial Life journal. Data: 34 pages, 2 figure

    Adapting specifications for reactive controllers

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    For systems to respond to scenarios that were unforeseen at design time, they must be capable of safely adapting, at runtime, the assumptions they make about the environment, the goals they are expected to achieve, and the strategy that guarantees the goals are fulfilled if the assumptions hold. Such adaptation often involves the system degrading its functionality, by weakening its environment assumptions and/or the goals it aims to meet, ideally in a graceful manner. However, finding weaker assumptions that account for the unanticipated behaviour and of goals that are achievable in the new environment in a systematic and safe way remains an open challenge. In this paper, we propose a novel framework that supports assumption and, if necessary, goal degradation to allow systems to cope with runtime assumption violations. The framework, which integrates into the MORPH reference architecture, combines symbolic learning and reactive synthesis to compute implementable controllers that may be deployed safely. We describe and implement an algorithm that illustrates the working of this framework. We further demonstrate in our evaluation its effectiveness and applicability to a series of benchmarks from the literature. The results show that the algorithm successfully learns realizable specifications that accommodate previously violating environment behaviour in almost all cases. Exceptions are discussed in the evaluation

    Tools and Algorithms for the Construction and Analysis of Systems

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    This open access book constitutes the proceedings of the 28th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2022, which was held during April 2-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 46 full papers and 4 short papers presented in this volume were carefully reviewed and selected from 159 submissions. The proceedings also contain 16 tool papers of the affiliated competition SV-Comp and 1 paper consisting of the competition report. TACAS is a forum for researchers, developers, and users interested in rigorously based tools and algorithms for the construction and analysis of systems. The conference aims to bridge the gaps between different communities with this common interest and to support them in their quest to improve the utility, reliability, exibility, and efficiency of tools and algorithms for building computer-controlled systems

    Quantitative Analyses of Software Product Lines

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    A software product-line (SPL) is a family of related software systems that are jointly developed and reuse a set of shared assets. Each individual software system in an SPL is called a software product and includes a set of mandatory and optional features, which are independent units of functionality. Software-analysis techniques, such as model checking, analyze a model of a software system to determine whether the software system satisfies its requirements. Because many software-analysis techniques are computationally intensive, and the number of software products in an SPL grows exponentially with the number of features in an SPL, it tends to be very time consuming to individually analyze each product of an SPL. Family-based analyses have adapted standard software-analysis techniques (e.g., model checking, type checking) to simultaneously analyze all of the software products in an SPL, reusing partial analysis results between different software products to speed up the analysis. However, these family-based analyses verify only the functional requirements of an SPL, and we are interested in analyzing the quality of service that different software products in an SPL would exhibit. Quantitative analyses of a software system model (e.g., of a weighted transition system) can estimate how long a system will take to reach its goal, how much energy a system will consume, and so on. Quantitative analyses are known to be computationally intensive. In this thesis, we investigate whether executing a family-based quantitative analysis on a model of an SPL is faster than individually analyzing every software product of the SPL. First, we present a family-based trace-checking analysis that facilitates the reconfig- uration of a dynamic software product line (DSPL), which is a type of SPL in which features can be activated or deactivated at runtime. We assessed whether executing the family-based trace-checking analysis is faster than executing the trace-checking analysis on every software product in three case studies. Our results indicated that the family-based trace checking analysis, when combined with simple data-abstraction over an SPL model’s quality-attribute values to facilitate sharing of partial-analysis results, is between 1.4 and 7.7 times faster than individually analyzing each software product. This suggests that abstraction over the quality-attribute values is key to make family-based trace-checking analysis efficient. Second, we consider an SPL’s maximum long-term average value of a quality attribute (e.g., because it represents the long-term rate of energy consumption of the system). Specifically, the maximum limit-average cost of a weighted transition represents an upper bound on the long-term average value of a quality attribute over an infinite execution of the system. Because computing the maximum limit-average cost of a software system is computationally intensive, we developed a family-based analysis that simultaneously computes the maximum limit-average cost for each software product in an SPL. We assessed its per- formance compared to individually analyzing each software product in two case studies. Our results suggest that our family-based analysis will perform best in SPLs in which many products share the same set of strongly connected components. Finally, because both of our family-based analyses require as input a timed (weighted) behaviour model of a Software Product Line, we present a method to learn such a timed (weighted) behaviour model. Specifically, the objective is to learn, for each transition t, a regression function that maps a software product to a real-valued weight that represents the duration of transition t’s execution in that software product. We apply supervised learning techniques, linear regression and regularized linear regression, to learn such functions. We assessed the accuracy of the learnt models against ground truth in two different SPL and also compared the accuracy of our method against two different state-of-the-art methods: Perfume and a Performance-Influence model. Our results indicate that the accuracy of our learnt models ranged from a mean error of 3.8% to a mean error of 193.0%. Our learnt models were most accurate for those transitions whose execution times had low variance across repeated executions of the transition in the same software product, and in which there is a linear relationship between the transition’s execution time and the presence of features in a software product

    Performance Evaluation of Transition-based Systems with Applications to Communication Networks

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    Since the beginning of the twenty-first century, communication systems have witnessed a revolution in terms of their hardware capabilities. This transformation has enabled modern networks to stand up to the diversity and the scale of the requirements of the applications that they support. Compared to their predecessors that primarily consisted of a handful of homogeneous devices communicating via a single communication technology, today's networks connect myriads of systems that are intrinsically different in their functioning and purpose. In addition, many of these devices communicate via different technologies or a combination of them at a time. All these developments, coupled with the geographical disparity of the physical infrastructure, give rise to network environments that are inherently dynamic and unpredictable. To cope with heterogeneous environments and the growing demands, network units have taken a leap from the paradigm of static functioning to that of adaptivity. In this thesis, we refer to adaptive network units as transition-based systems (TBSs) and the act of adapting is termed as transition. We note that TBSs not only reside in diverse environment conditions, their need to adapt also arises following different phenomena. Such phenomena are referred to as triggers and they can occur at different time scales. We additionally observe that the nature of a transition is dictated by the specified performance objective of the relevant TBS and we seek to build an analytical framework that helps us derive a policy for performance optimization. As the state of the art lacks a unified approach to modelling the diverse functioning of the TBSs and their varied performance objectives, we first propose a general framework based on the theory of Markov Decision Processes. This framework facilitates optimal policy derivation in TBSs in a principled manner. In addition, we note the importance of bespoke analyses in specific classes of TBSs where the general formulation leads to a high-dimensional optimization problem. Specifically, we consider performance optimization in open systems employing parallelism and closed systems exploiting the benefits of service batching. In these examples, we resort to approximation techniques such as a mean-field limit for the state evolution whenever the underlying TBS deals with a large number of entities. Our formulation enables calculation of optimal policies and provides tangible alternatives to existing frameworks for Quality of Service evaluation. Compared to the state of the art, the derived policies facilitate transitions in Communication Systems that yield superior performance as shown through extensive evaluations in this thesis

    Creating Through Mind and Emotions

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    The texts presented in Proportion Harmonies and Identities (PHI) Creating Through Mind and Emotions were compiled to establish a multidisciplinary platform for presenting, interacting, and disseminating research. This platform also aims to foster the awareness and discussion on Creating Through Mind and Emotions, focusing on different visions relevant to Architecture, Arts and Humanities, Design and Social Sciences, and its importance and benefits for the sense of identity, both individual and communal. The idea of Creating Through Mind and Emotions has been a powerful motor for development since the Western Early Modern Age. Its theoretical and practical foundations have become the working tools of scientists, philosophers, and artists, who seek strategies and policies to accelerate the development process in different contexts

    Information between Data and Knowledge: Information Science and its Neighbors from Data Science to Digital Humanities

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    Digital humanities as well as data science as neighboring fields pose new challenges and opportunities for information science. The recent focus on data in the context of big data and deep learning brings along new tasks for information scientist for example in research data management. At the same time, information behavior changes in the light of the increasing digital availability of information in academia as well as in everyday life. In this volume, contributions from various fields like information behavior and information literacy, information retrieval, digital humanities, knowledge representation, emerging technologies, and information infrastructure showcase the development of information science research in recent years. Topics as diverse as social media analytics, fake news on Facebook, collaborative search practices, open educational resources or recent developments in research data management are some of the highlights of this volume. For more than 30 years, the International Symposium of Information Science has been the venue for bringing together information scientists from the German speaking countries. In addition to the regular scientific contributions, six of the best competitors for the prize for the best information science master thesis present their work
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