1,581 research outputs found

    Semantic technologies for open interaction systems

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    Open interaction systems play a crucial role in agreement technologies because they are software devised for enabling autonomous agents (software or human) to interact, negotiate, collaborate, and coordinate their activities in order to establish agreements and manage their execution. Following the approach proposed by the recent literature on agent environments those open distributed systems can be efficiently and effectively modeled as a set of correlated physical and institutional spaces of interaction where objects and agents are situated. In our view in distributed open systems, spaces are fundamental for modeling the fact that events, actions, and social concepts (like norms and institutional objects) should be perceivable only by the agents situated in the spaces where they happen or where they are situated. Institutional spaces are also crucial for their active functional role of keeping track of the state of the interaction, and for monitoring and enforcing norms. Given that in an open distributed and dynamic system it is fundamental to be able to create and destroy spaces of interaction at run-time, in this paper we propose to create them using Artificial Institutions (AIs) specified at design time. This dynamic creation is a complex task that deserves to be studied in all details. For doing that, in this paper, we will first define the various components of AIs and spaces using Semantic Web Technologies, then we will describe the mechanisms for using AIs specification for realizing spaces of interaction. We will exemplify this process by formalizing the components of the auction Artificial Institution and of the spaces created for running concrete auction

    Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

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    Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

    mSpace meets EPrints: a Case Study in Creating Dynamic Digital Collections

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    In this case study we look at issues involved in (a) generating dynamic digital libraries that are on a particular topic but span heterogeneous collections at distinct sites, (b) supplementing the artefacts in that collection with additional information available either from databases at the artefact's home or from the Web at large, and (c) providing an interaction paradigm that will support effective exploration of this new resource. We describe how we used two available frameworks, mSpace and EPrints to support this kind of collection building. The result of the study is a set of recommendations to improve the connectivity of remote resources both to one another and to related Web resources, and that will also reduce problems like co-referencing in order to enable the creation of new collections on demand

    Real “Smart Cities”: Insights from Civitas PROSPERITY

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    A city does not need to be smart, but to allow people be, behave, live and work smart(er). Furthermore, smart should not be necessarily equalled to high technology, but to the sound management, communication and use of available resources, be they tangible or intangible. Anyway our evolution cannot be limited to technology, even if the latter has become unavoidable. If not accompanied by a comprehensive perspective and coherent management, technology may rather block than facilitate resilience and sustainable urban development. Not always the most technically advanced and expensive solutions are the best (most effective) ones or frequently they cannot work alone, needing to be complemented by soft / lower-cost measures. Moreover,even if the actual “smart city” paradigm would be accepted, there do not seem to be enough resources (especially primary ones) to provide high-tech for everybody (WWF, 2018). In this case high-tech might be replaced by smart-tech staying for innovative solutions of best coping with given situations no matter the level of scientific, cultural, economic and behavioural advancement. These are some of the conclusions of a recent ongoing project funded through Horizon 2020, pleading for a global integrated perspective and providing the appropriate tools to sustainably shape and enhance it. Being built in response to the challenge “Real Smart Cities. Best practices and concepts for the future”, the present contribution informs on how Civitas PROSPERITY (applied research project) integrated these principles and produced innovation in the field of Sustainable Urban Mobility Plans (SUMP). The focus is on bright solutions that can be equally extended and applied in other fields of urban management beyond mobility, such as energy, land-use, cultural heritage etc

    Agent-Environment Interaction in MAS - Introduction and Survey

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    Regulated MAS: Social Perspective

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    This chapter addresses the problem of building normative multi-agent systems in terms of regulatory mechanisms. It describes a static conceptual model through which one can specify normative multi-agent systems along with a dynamic model to capture their operation and evolution. The chapter proposes a typology of applications and presents some open problems. In the last section, the authors express their individual views on these mattersMunindar Singh’s effort was partially supported by the U.S. Army Research Office under grant W911NF-08-1-0105. The content of this paper does not necessarily reflect the position or policy of the U.S. Government; no official endorsement should be inferred or implied. Nicoletta Fornara’s effort is supported by the Hasler Foundation project nr. 11115-KG and by the SER project nr. C08.0114 within the COST Action IC0801 Agreement Technologies. Henrique Lopes Cardoso’s effort is supported by Fundação para a Ciência e a Tecnologia (FCT), under project PTDC/EIA-EIA/104420/2008. Pablo Noriega’s effort has been partially supported by the Spanish Ministry of Science and Technology through the Agreement Technologies CONSOLIDER project under contract CSD2007-0022, and the Generalitat of Catalunya grant 2009-SGR-1434.Peer Reviewe

    A Survey of Semantic Metadata Management Models for the Social Web

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    12Social systems are a new generation of Web 2.0 applications, characterized by their primarily user-driven content and the ability to mediate personal and social information across communities, such as teams, communities, and organizations. The recent growth and adaptation of social systems for personal and social information management has created new opportunities for users to be producers as well as consumers of information. This paper aims at studying the different models that have been proposed to better connect resources, annotations and users and their usage in the social Web. The paper aims to answer questions like: \textit{What are the existing models that allow to semantically describe resources, users and tags in the social Web? What are the characteristics of such models? What are the differences between those models?} The final objective is to provide an understandable study and comparison of some of the existing models to help researchers, and developers, to make their decision whenever there is a need to use a semantic meta-data model in the social Web. More concretely, this work aims to be a reference guide for different professionals in order to accelerate the adoption of such technologies in the Social Web

    Roadmap for KRSM RTD

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    ID5.2 Roadmap for KRSM RTD

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    Roadmap for KRSM RTD activities.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

    Multi-Agent Systems

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    This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019
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