We explain basic features of an emerging area called Intelligent Environments. We give a short overview on how it has developed, what is the current state of the art and what are the challenges laying ahead. The aim of the article is to make aware the Computer Science community of this new development, the differences with previous dominant paradigms and the opportunities that this area offers to the scientific community and society. Basic conceptsHere we explain how the area of Intelligent Environments (IE) has developed, what its core values are and how it differs from other areas. By "Environment" we refer here to any space in our surroundings. Although some people may also consider virtual environments here we mostly refer to Physical spaces, in all its diversity, e.g., house, building, street, a field, an area in the sea or space, etc. Our use of the word "Intelligent" applied to Environments mostly refers to Artificial Intelligence, as defined in [1]. An Intelligent Environment is one in which the actions of numerous networked controllers (controlling different aspects of an environment) is orchestrated by self-programming pre-emptive processes (e.g., intelligent software agents) in such a way as to create an interactive holistic functionality that enhances occupants experiences. Historical development of the areaFor centuries humans have witnessed scientific and technological leaps that changed the lives of their generation, and those to come, forever. We are no exception. In fact many of those advances are occurring now, in a more or less unperceivable way. Slowly and silently technology is becoming interwoven in our lives in the form of a variety of devices which are starting to be used by people of all ages and as part of their daily routine. As predicted by M. Weiser [2], this technology is gradually disappearing from our cognitive front, as we increasingly take for granted its existence. But this fact alone could not justify a paradigm shift, as we claim in this manifesto.The emergence of a new paradigm requires the convergence of various domains of human activity, many of which are not technological. It is true that numerous technological advances have taken place during the past two decades worldwide, mainly due to persistent efforts by researchers and systematic funding by governments and markets. Among these advances one could site:

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The main problem that systems theory tries to solve is the problem of complexity. The notion of complexity is very often correlated to variables such as entropy and energy, organization and disorganization and others that eventually converge to the common ground of comprehensibility or incomprehensibility. In this paper, we try to shed light on the notions of entropy and energy as they should be conceived in the theoretical framework of social sciences. We analyse the different meanings of entropy, dealing with the social systems as if they were information management systems (following Niklas Luhmann), and we also present an initial approach, as to the meaning of energy for those systems, bringing Pierre Bourdieu's theory of symbolic capital into a Luhmannian context.

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The vision of ubiquitous computing is that the addition of computation and communication abilities to the artifacts that surround people will enable the users to set up their living spaces in a way that will serve them best minimising at the same time the required human intervention. The ontologies can help us to address some key issues of ubiquitous computing environments such as knowledge representation, semantic interoperability and service discovery.The GAS Ontology is an ontology that was developed in order to describe the semantics of the basic concepts of a ubiquitous computing environment and define their inter-relations. The basic goal of this ontology is to provide a common language for the communication and collaboration among the heterogeneous devices that constitute these environments. The GAS Ontology also supports the service discovery mechanism that a ubiquitous computing environment requires.In this paper we present the GAS Ontology as well as the design challenges that we faced and the way that we handled them. In order to select the language and the tool that we used for the development of the GAS Ontology, we designed a prototype ontology and evaluated a number of languages and tools. The ontology development tool that proved to be the most suitable from this evaluation was Protégé-2000. We also present how we use the GAS Ontology in our eGadgets project achieving semantic interoperability and service discovery. Finally, we present the GAS Ontology manager, which runs on each device, manages the device's ontology and processes the knowledge that each device acquires over time.

This paper aims to provide the reader with a comprehensive background for understanding current knowledge on Academic Advising Systems (AAS) and its impact on learning. It constitutes an overview of empirical evidence behind key objectives of the potential adoption of AAS in generic educational strategic planning. The researchers examined the literature on experimental case studies conducted in the domain during the past ten years (2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017). Search terms identified 98 mature pieces of research work, but inclusion criteria limited the key studies to 43. The authors analyzed the research questions, methodology, and findings of these published papers and categorized them accordingly. The results have highlighted three distinct major directions of the AAS empirical research. This paper discusses the emerged added value of AAS research and highlights the significance of further implications. Finally, the authors set their thoughts on possible uncharted key questions to investigate both from pedagogical and technical considerations.

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Our research has been performed in the context of the EU-funded R&D project PLANTS. In this paper, we describe an ontology-driven architecture for developing systems that can be used in precision agriculture applications. Central to our approach is the use of an ontology, which views plants and associated computation as an integral part and allows the interaction of plants and artefacts in the form of synergistic mixed societies. PLANTS ontology sets up a conceptual framework that combines the knowledge about sensors, actuators and other domain concepts available, on the one hand, and the biological studies about plant stressing and sensing mechanisms and consequent plant behaviour, on the other hand, to make plants a proactive component of agricultural systems.